Science, Irving K. Barber Faculty of (Okanagan)
Computer Science, Mathematics, Physics and Statistics, Department of (Okanagan)
DSpace
UBCO
Chowdhury, Sohan
Master of Science - MSc
University of British Columbia
Teleportation is a widely used locomotion technique in Virtual Reality (VR) that allows users to move through a virtual environment. Traditionally, handheld controllers have been used for teleportation, however, recent advancements in VR hand tracking have opened the opportunity for more natural, intuitive and immersive controller-free teleportation. In this thesis, I design and develop novel hand-based teleportation techniques allowing users to perform teleportations in VR only using their hands without using any controllers. I explore two core aspects: (i) WriArm: Using hand gestures with combined wrist and arm movements for a complete controller-free hands-only teleportation technique in VR, (ii) PAWS: Considering different physical attributes of users to personalize the controller-free system to each user. Results show WriArm is 9% faster than the arm-based technique, and PAWS improves a further 10% over WriArm, especially for difficult destinations. Users also preferred my techniques and found them easier to use, making them a viable hand-based controller-free alternative for navigating VR environments. I conclude with design considerations for hands-only teleportation design for controller-free virtual reality.
Exploring Personalized Hands-OnlyTeleportation in Virtual RealitybySohan ChowdhuryA THESIS SUBMITTED IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OFMASTER OF SCIENCEinTHE COLLEGE OF GRADUATE STUDIES(Computer Science)THE UNIVERSITY OF BRITISH COLUMBIA(Okanagan)April 2023© Sohan Chowdhury, 2023The following individuals certify that they have read, and recommendto the College of Graduate Studies for acceptance, a thesis/dissertation en-titled:Exploring Personalized Hands-Only Teleportation in VirtualRealitysubmitted by Sohan Chowdhury in partial fulfillment of the requirements ofthe degree of Master of Science.Dr. Mohammad Khalad Hasan, CMPS, Irving K. Barber Faculty of ScienceSupervisorDr. Ramon Lawrence, CMPS, Irving K. Barber Faculty of ScienceSupervisory Committee MemberDr. Abbas Milani, School of Engineering, Faculty of Applied ScienceSupervisory Committee MemberDr. Megan Smith, Faculty of Creative and Critical StudiesUniversity ExamineriiAbstractTeleportation is a widely used locomotion technique in Virtual Reality(VR) that allows users to move through a virtual environment. Tradition-ally, handheld controllers have been used for teleportation, however, recentadvancements in VR hand tracking have opened the opportunity for morenatural, intuitive and immersive controller-free teleportation. In this the-sis, I design and develop novel hand-based teleportation techniques allowingusers to perform teleportations in VR only using their hands without usingany controllers. I explore two core aspects: (i) WriArm: Using hand ges-tures with combined wrist and arm movements for a complete controller-freehands-only teleportation technique in VR, (ii) PAWS: Considering differentphysical attributes of users to personalize the controller-free system to eachuser. Results show WriArm is 9% faster than the arm-based technique,and PAWS improves a further 10% over WriArm, especially for difficultdestinations. Users also preferred my techniques and found them easier touse, making them a viable hand-based controller-free alternative for navigat-ing VR environments. I conclude with design considerations for hands-onlyteleportation design for controller-free virtual reality.iiiLay SummaryMoving around large virtual worlds without a corresponding large openphysical area in the real world is a critical issue in Virtual Reality (VR).Currently, “Teleportation” is the most commonly used solution to addressthe issue where users use handheld VR controllers to navigate to the virtualworld where with the controller, they point to a destination and then pressa button to move there instantly. New advances in VR headset technologynow allow people to use their hands instead of controllers for VR expe-riences. In my thesis, I propose new ways of performing “Teleportation”using users’ hands only without requiring any controller. Specifically, I ex-plore ways to leverage hand gestures, wrist and arm movements to navigatein VR. Additionally, I investigate how to design hand-based teleportationsolutions considering people with different physical attributes (e.g., peoplewith various arm and wrist movement ranges), thus providing users withsimilar teleportation experiences regardless of their physical characteristics.Results from user studies show that my techniques are faster than exist-ing hand-based teleportation techniques for navigating VR environments. Iconclude with design considerations for hands-only teleportation design forcontroller-free virtual reality.ivPrefaceThis thesis is the original work by the author, Sohan Chowdhury. Thisstudy was granted approval by the University of British Columbia Be-havioural Research Ethics Board- Certificate Number: H18-01984.One chapter of this thesis has already been published in a conference pro-ceeding and another chapter has been accepted, pending publication:[Published] Chowdhury, S., Ullah, A. K. M. A., Pelmore, N. B., Irani, P.,and Hasan, K. Wriarm: Leveraging Wrist Movement to Design Wrist+ArmBased Teleportation in VR. In 2022 IEEE International Symposium onMixed and Augmented Reality (ISMAR) (2022), pp. 317 – 325.[Accepted] Chowdhury, S., Delamare, W., Irani, P., and Hasan, K.PAWS: Personalized Arm and Wrist Movements With Sensitivity Mappingsfor Controller-Free Locomotion in Virtual Reality. In 2023 ACM Inter-national Conference on Mobile Human-Computer Interaction (MobileHCI)(2023), 18 pages.vTable of ContentsAbstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iiiLay Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ivPreface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vTable of Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . viList of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ixAcknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . xiiDedication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiiiChapter 1: Introduction . . . . . . . . . . . . . . . . . . . . . . . 11.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.3 Research Questions . . . . . . . . . . . . . . . . . . . . . . . . 31.4 Research Approach . . . . . . . . . . . . . . . . . . . . . . . . 31.5 Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . 71.6 Thesis Overview . . . . . . . . . . . . . . . . . . . . . . . . . 8Chapter 2: Related Work . . . . . . . . . . . . . . . . . . . . . . 92.1 Locomotion in Virtual Reality . . . . . . . . . . . . . . . . . . 102.1.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . 102.1.2 Continuous Locomotion . . . . . . . . . . . . . . . . . 112.1.3 Instant Locomotion . . . . . . . . . . . . . . . . . . . 142.2 Controller-Free Hands-Only Virtual Reality . . . . . . . . . . 152.2.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . 152.2.2 Gestures . . . . . . . . . . . . . . . . . . . . . . . . . . 152.2.3 Locomotion . . . . . . . . . . . . . . . . . . . . . . . . 16viTABLE OF CONTENTS2.2.4 Teleportation . . . . . . . . . . . . . . . . . . . . . . . 172.2.5 Personalization . . . . . . . . . . . . . . . . . . . . . . 18Chapter 3: Designing Controller Free Teleportation . . . . . . 203.1 Design Study 1: Exploring Hand Gestures . . . . . . . . . . . 213.1.1 Participants . . . . . . . . . . . . . . . . . . . . . . . . 233.1.2 Procedure . . . . . . . . . . . . . . . . . . . . . . . . . 233.1.3 Data Collection . . . . . . . . . . . . . . . . . . . . . . 233.1.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . 243.2 Techniques for Controlling the Teleportation Pointer . . . . . 253.3 User Study 2: Evaluating User Performance . . . . . . . . . . 273.3.1 Participants . . . . . . . . . . . . . . . . . . . . . . . . 273.3.2 Apparatus . . . . . . . . . . . . . . . . . . . . . . . . . 273.3.3 Tasks and Procedure . . . . . . . . . . . . . . . . . . . 273.3.4 Design . . . . . . . . . . . . . . . . . . . . . . . . . . . 293.3.5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . 313.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34Chapter 4: Personalizing Hand-Based Teleportation . . . . . . 354.1 Study 3: Exploring Different Levels of Personalization onTeleportation . . . . . . . . . . . . . . . . . . . . . . . . . . . 364.1.1 Hands-Only Parabola Pointer Control . . . . . . . . . 364.1.2 Origin and Direction . . . . . . . . . . . . . . . . . . . 364.1.3 Personalizing Parabola Controls . . . . . . . . . . . . 384.1.4 Participants . . . . . . . . . . . . . . . . . . . . . . . . 404.1.5 Apparatus . . . . . . . . . . . . . . . . . . . . . . . . . 404.1.6 Experimental Task, Procedure and Design . . . . . . . 404.1.7 Results . . . . . . . . . . . . . . . . . . . . . . . . . . 444.1.8 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . 464.2 Study 4: Exploring Sensitivity Functions . . . . . . . . . . . . 474.2.1 Sensitivity Functions . . . . . . . . . . . . . . . . . . . 484.2.2 Association Between Motor Movements and Sensitiv-ity Functions . . . . . . . . . . . . . . . . . . . . . . . 484.2.3 Evaluated Techniques . . . . . . . . . . . . . . . . . . 494.2.4 Participants . . . . . . . . . . . . . . . . . . . . . . . . 514.2.5 Experimental Tasks, Procedure, and Design . . . . . . 524.2.6 Results . . . . . . . . . . . . . . . . . . . . . . . . . . 534.2.7 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . 55Chapter 5: Design Guidelines, Limitations and Future Work 57viiTABLE OF CONTENTS5.1 Design Guidelines . . . . . . . . . . . . . . . . . . . . . . . . . 575.1.1 Gestures for Teleportaion . . . . . . . . . . . . . . . . 575.1.2 Hand-Based Teleportation Pointing . . . . . . . . . . . 585.1.3 Task Design . . . . . . . . . . . . . . . . . . . . . . . . 595.1.4 Physical Attributes And Personalization . . . . . . . . 595.2 Limitations and Future Work . . . . . . . . . . . . . . . . . . 615.2.1 Technique . . . . . . . . . . . . . . . . . . . . . . . . . 615.2.2 Study . . . . . . . . . . . . . . . . . . . . . . . . . . . 62Chapter 6: Conclusion . . . . . . . . . . . . . . . . . . . . . . . . 65Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68viiiList of FiguresFigure 1.1 WriArm teleportation technique. (a-c) Third-personviews show users’ wrist and arm movements to controlthe parabolic pointer for teleportation in VR. . . . . 3Figure 1.2 WriArm teleportation technique. (a,b) A user is usingWriArm to teleport to different locations . . . . . . . 4Figure 1.3 (a-b) Two users with different physical attributes suchas differences in height, arm reach (horizontal andvertical) and wrist angle range. . . . . . . . . . . . . . 5Figure 1.4 (a-c) Demonstrating personalization. (a) Users withspecific physical attributes might find it easy to tele-port, (b) while users with different physical attributesmight find it difficult. (c) My proposed personal-ized teleportation technique, PAWS, that takes differ-ences into account and personalizes the teleportationparabola so that every user, regardless of their phys-ical attributes, can perform the teleportations easily. . 6Figure 2.1 An overview of the relevant locomotion techniquesand some of their key attributes which are exploredin this thesis. PAWS is the technique designed anddeveloped as the final product of this thesis. Emptyboxes denote attributes that have results which aremixed or not applicable. . . . . . . . . . . . . . . . . 10Figure 3.1 Selected gesture sets for teleportations after the de-sign study . . . . . . . . . . . . . . . . . . . . . . . . 22Figure 3.2 Participants preference ratings across the gesture sets 24Figure 3.3 (a-b) Arm-based teleportation technique. . . . . . . 25ixLIST OF FIGURESFigure 3.4 Teleportation height and distance. (a) The user needsto teleport from 0m to 0m height with a distance of5.5m and (b) from 0m to 5m height with a distanceof 11m. . . . . . . . . . . . . . . . . . . . . . . . . . . 29Figure 3.5 Study results. Mean trial time (a) across the tech-niques and gesture sets. Results showed that Tech-nique had significant effects on trial time where userswere faster with WriArm than Arm as indicated byasterisks (*). I observed that Gesture Set had sig-nificant effects on trial time where users were fasterwith gesture set 4 than with gesture set 1 and gestureset 3. Mean trial time for (b) distance and height (inseconds). Users’ provided feedback on their prefer-ence, fatigue, speed, accuracy and easiness on a 5-point Likert scale across (c) three gesture sets and(d) two techniques. I observed that users’ ratings forWriArm were significantly higher than Arm on userpreference and easiness as indicated by asterisks. . . . 31Figure 3.6 Study results. Users’ provided feedback on their pref-erence, fatigue, speed, accuracy and easiness on a 5-point Likert scale across (a) three gesture sets and(b) two techniques. I observed that users’ ratings forWriArm were significantly higher than Arm on userpreference and easiness, as indicated by asterisks. . . 32Figure 4.1 (a) A user can control the ray origin and direction viaarm and wrist orientation. (b) Launch angle is con-trolled by the wrist’s upward/extension and down-ward/flexion movements. (c) The user can reach afurther distance via arm extension and retraction move-ments, and (d) by moving their hand vertically up ordown. . . . . . . . . . . . . . . . . . . . . . . . . . . . 37xLIST OF FIGURESFigure 4.2 Teleportation with Height variation and Distance fac-tors. (a) A user controls the pointer with arm andwrist movements (Height variation: from 0m to 5 mand Distance: 10.5 m). (b) The color of the parabolicpointer and the sphere changes from red to green onceparticipants successfully aim the pointer at the sphere(Height variation: from 0 m to 2.5 m and Distance:7.0 m). (c) The user is teleported to the target plat-form once they issue a hand gesture for selection (i.e.,closing their index finger) and looks down to the nexttarget location. (Height variation: from 5 m to 0 mand Distance: 3.5 m) . . . . . . . . . . . . . . . . . . 41Figure 4.3 Mean (a) trial time and (b) error rate across the tech-niques. Error bars show the 95% CIs. . . . . . . . . . 44Figure 4.4 (a) Users ratings on baseline, PAdist, and PAW basedon their preference, fatigue, speed, accuracy and eas-iness. . . . . . . . . . . . . . . . . . . . . . . . . . . . 46Figure 4.5 (a) The sensitivity curves for each motor movement.Appearing left to right in sequence: SSQ (Sigmoid-Sigmoid-Quadratic), IIQ (Inverse Sigmoid-Inverse Sigmoid-Quadratic), SIQ (Sigmoid-Inverse Sigmoid-Quadratic)and ISQ (Inverse Sigmoid-Sigmoid-Quadratic). . . . 49Figure 4.6 (a-c) The result of the sensitivity curve combinationsis shown with gradients. A stronger color in thegradient indicates higher stability (i.e., coarse move-ments), and a weaker color indicates lower stability(i.e., fine-grained movements). Note that the effectfor arm movement (Adist and Aori) is only shown in(a) SSQ and (b) IIQ, excluding the wrist movement(Wangle). (c) Wrist uses a quadratic function whereupward/extension and downward/flexion extreme an-gles benefit from a more stable control than the mid-dle range. . . . . . . . . . . . . . . . . . . . . . . . . . 50Figure 4.7 Mean trial time across the techniques for differentheight variations. Error bars show the 95% CIs. . . . 53Figure 4.8 Error rate across the techniques for different distances.Error bars show the 95% CIs. . . . . . . . . . . . . . 55xiAcknowledgementsI want to extend my sincere gratitude to Dr. Mohammad Khalad Hasan,my supervisor, for his unwavering support, advice, and assistance. Hisstrong work ethic, vast expertise, and passion for his field were a sourceof inspiration during the more difficult times in my thesis journey. His will-ingness to go the extra mile for me and his constant encouragement allowedme overcome many obstacles during my graduate studies. I am grateful forhis contribution and without it, I would not have been able to overcome theobstacles I faced in my thesis work.I would like to sincerely acknowledge the useful suggestions provided bymy committee members and the university examiner: Dr. Abbas Milani,Dr. Ramon Lawrence, and Dr. Megan Smith. I would also like to thankDr. Miles Thorogood for managing his time to chair the thesis defense.I gratefully acknowledge and thank Dr. William Delamare for his ex-tensive comments and feedback on PAWS, and Dr. Pourang Irani for hisfeedback on WriArm and PAWS. I also thank Nathan Pelmore and AmanatUllah for their valuable suggestions and support during the development ofWriArm.I owe a huge gratitude to my loving family for their unwavering love, thelab members for their constant support and Al-Amin Sabuj for his supportin a foreign country (and his late-night supply of amazing food in the lab).I also want to remember my dear friend and colleague Raihan Al-Mamun,who had a significant impact on my life prior to his untimely departure fromthis world.xiiDedicationI dedicate my thesis to my loving parents: Amina Chowdhury, AbuTaleb Chowdhury and my aunt: Shahin Akhter. I am specially gratefulto my mother, without whom I would not have been able to pursue myeducation in computer science and my aunt who supported and shelteredme like one of her own children for many years.xiiiChapter 1Introduction1.1 BackgroundInteraction in Virtual Reality (VR) environments commonly requiresusers to navigate through a large virtual space while staying fixed in a limitedphysical space, such as an indoor or a tracked area. Locomotion, definedas a self-propelled movement in VR [40], allows users to navigate virtualspaces.Experiencing and being immersed in a virtual world requires users to beable to move around (loco-mote) that world freely. Even though walkingand running in reality to move in the virtual world is an obvious solution,most users do not have access to large open spaces to walk and run in thereal world for their VR experiences. Locomotion is considered one of themost common tasks in VR [8] and locomotion techniques are a crucial partof VR because they allow users to travel across large virtual worlds whilebeing in limited physical space. They are used in many VR applications andgames where the user has to move around in the environment.Researchers have explored different locomotion techniques, to tackle is-sues such as limited space, special hardware requirements, immersion, fa-tigue and ease of use. Some of the prominent techniques are Flying [79]where users move smoothly along the environment as if they were flying,Walking-In-Place [87] where users move their legs up and down in place tomove in the virtual world, Redirected Walking [76] where visual perceptionmanipulation is used to move the user in a circular path in a limited spaceand Teleportation [8] where users move instantly to their destination.Teleportation has been reported as one of the primary techniques for VRlocomotion due to its minimal impact on motion sickness, being usable invery limited space while seated or standing, and its general ease of use [33].Teleportation in VR applications is commonly performed with a physicalcontroller [33], where users point to a teleportation spot with a parabolicpointer (emanating from the controller) and press a button to be instantlyrelocated. However, prior research showed that holding a physical controllerlimits natural and immersive interactions, such as manipulating objects in11.2. MOTIVATIONVR, and makes the overall experience less intuitive [14, 93].1.2 MotivationWith the recent availability of on-device hand-tracking solutions in VRheadsets, hands-only teleportation is becoming a viable controller-free al-ternative. Hands-only methods relieve the user from having to hold a con-troller allowing natural and rapid input (i.e. without needing to strap on adevice). Prior work has shown that such methods are intuitive and easy touse for VR interaction and offer a rich design space [11, 48, 81, 83, 86]. Re-searchers also explored different hands-only locomotion solutions leveraginghand movements [11, 47, 50, 83, 94] to navigate from one location to anotherin virtual environments. Results show that bare-hand techniques lead toless motion-sickness and lower task loads than controller-based techniques,while still providing intuitive and immersive experiences [50, 67]. However,researchers have also suggested that controllers and device-based interac-tions can be cumbersome [69] whereas hand-based methods are intuitive,natural, and easy to use [48] for VR interactions such as grabbing, pickingup objects, and object manipulation [66]. Without hand-based locomotion,such natural interactions are strictly limited as users would need to pick upa controller, perform locomotion, and remove the controllers again to freetheir hands for natural hand interactions. Hand-based teleportation allowsusers to move around the virtual world and perform natural hand-basedinteractions with all objects near and far without ever requiring controllers.Prior work exploring controller-free teleportation techniques leveraginghand tracking solutions relied mostly on dwell-based (timeout) [11, 62, 83]confirmations and do not account for interference with other natural handgestures. Prior work also only uses arm movement to point towards theintended teleport destination [11, 83], and reported that the arm movementlimits the teleportation activities, especially when it requires users to beteleported to a higher elevation (e.g., stairs), and impacts overall users’ per-formance [11, 62]. Using the wrist is a viable option for providing moreprecise pan and tilt control in hand-based teleport but it remains unex-plored. Realistic navigation tasks that include combinations of height anddistance changes have also not been explored. When using hand-trackingsolutions, instead of uniformly made hardware controllers, the system de-pends on each users physical attributes. Differences in those attributes suchas users’ height, arm, and wrist movement ranges also become a factor whenusing hands instead of controllers. To my knowledge, these issues have not21.3. RESEARCH QUESTIONSbeen previously explored for hands-only teleportation.1.3 Research QuestionsIn this thesis, I am motivated by the hypothesis that using hand gesturesand combined wrist and arm movements can provide a fast, hands-only,controller-free teleportation experience to users regardless of any differencesin the users’ physical attributes such as their height or arm movement range.However, there are some gaps in previous work I need to explore to validatethis hypothesis, leading to two research questions:− RQ1: How can hand gestures along with arm and wrist movementsbe used instead of controllers to perform navigation tasks using VRteleportation?− RQ2: How can hands-only teleportation be personalized to a combi-nation of users’ physical attribute(s) to provide an improved telepor-tation experience for all users?Figure 1.1: WriArm teleportation technique. (a-c) Third-person views showusers’ wrist and arm movements to control the parabolic pointer for tele-portation in VR.1.4 Research ApproachRQ1: I designed and developed WriArm: a novel teleportation tech-nique that leverages wrist angle in combination with arm movement toprovide users with improved control over the movement of the parabolicpointer. More specifically, users can use arms for coarse pointer movements31.4. RESEARCH APPROACHFigure 1.2: WriArm teleportation technique. (a,b) A user is using WriArmto teleport to different locationswhile leveraging wrist angles for fine controlling of the parabolic pointer,thus adding more flexibility for precise control over the pointer for tele-portation (see Figure 1.1 and 1.2). While designing WriArm, I found noprior work exploring suitable hand gestures for initiating and executing keyteleportation activities, including pointer activation, pointing, confirmation,and cancellation. Therefore, I first conducted a design study with designersexploring suitable hand gestures for WriArm and arm-based teleportationactivities. I collected a set of 25 gestures for each teleportation activity andrefined the set further by selecting the top three gestures for each activitywhich were preferred by designers. I further ran user study 2, and evaluatedthe performance of WriArm and arm-based pointing in teleportation taskswith three gesture sets. I used tasks where users are required to navigatea VR environment with varying distances, directions, and heights. Resultsshowed that WriArm is significantly faster than the arm-based technique,answering RQ1.RQ2: Though WriArm can be considered as a potential alternative tocontrollers, it has an inherent dependency on users’ physical attributes suchas arm flexibility, wrist flexibility, and range of movements. For instance,arm and wrist movements are leveraged in hand-based techniques to controlthe teleportation pointer [11, 83]. However, the comfortable maxima andminima of arm movements (dependent on arm span and shoulder flexibility)and wrist angles can differ from user to user [74, 77].Thus, different people may experience the same hands-only teleportation41.4. RESEARCH APPROACHFigure 1.3: (a-b) Two users with different physical attributes such as differ-ences in height, arm reach (horizontal and vertical) and wrist angle range.technique differently (Figure 1.3 a, b) based on their physical abilities andattributes (Figure 2.1 a, b). Thus, a teleportation technique may work wellfor one user; however, it may not provide a similar experience to anotheruser with different physical attributes.Personalization and adaptation of techniques according to users’ at-tributes have been explored for various interactions such as cursor control in2D interfaces [46], prototyping user interfaces for Augmented Reality [12],generating custom game scenarios for Virtual Reality [71] and as a generalUI consideration [1, 36, 37, 59, 60]. Adapting techniques to users’ phys-ical and/or perceptual characteristics can lead to improved performances[49] and usability [46]. Although personalized applications exist for VRcontexts, to the best of my knowledge, no prior work explored leveragingpersonalized arm and wrist movements to help users navigate in VR.I report on two user studies exploring RQ2, regarding the implementa-tion, benefits and drawbacks of teleportation personalization. User study 3explores possible combinations of physical attributes available for personal-ization. I investigate and compare no-personalization, partial personaliza-tion (i.e., only arm vertical range), and full personalization (i.e., wrist andarm ranges personalization). Results reveal that users are faster with fullpersonalization than with the other two techniques. However, with all tech-niques, there is an increase in sensitivity due to the re-mapping of the motorrange, and performance degrades for distant targets. This effect is due toreduced motor stability and control in poses required to reach such distanttargets. Remedying increased sensitivity which induces rapid, less precise51.4. RESEARCH APPROACHFigure 1.4: (a-c) Demonstrating personalization. (a) Users with specificphysical attributes might find it easy to teleport, (b) while users with differ-ent physical attributes might find it difficult. (c) My proposed personalizedteleportation technique, PAWS, that takes differences into account and per-sonalizes the teleportation parabola so that every user, regardless of theirphysical attributes, can perform the teleportations easily.61.5. CONTRIBUTIONcontrol and restricts users having lower ranges of motion for hand-basedteleportation, also remains unexplored.I hence perform user study 4 to further explore different sensitivity map-ping functions (e.g., sigmoid, inverse-sigmoid, quadratic) for three movementranges: arm extension/retraction range, arm vertical movement range andwrist extension/flexion range. Each sensitivity function combination leadsto the parabolic pointer movement becoming more or less stable (i.e., preciseor coarse) in response to hand movements in different ranges of movements.Results show that users’ teleportation performance improved when usingfull personalization with adapted sensitivity mapping functions (answeringRQ2). Specifically, I found that using fully personalized wrist and armranges with a combination of the sigmoid and inverse sigmoid mappings forarm movements and quadratic mapping for wrist movements resulted in afaster and improved teleportation experience for users.1.5 ContributionTo summarize, my contributions to this thesis are:1. WriArm: a novel hand-based implementation for teleportation in VR(a) Design a user study evaluating suitable gesture sets that canbe used as an alternative to controllers for teleportation sub-activities: activation, pointing, confirmation and cancellation;(b) Design and evaluation of a novel hands-only technique using user’sarm and wrist movement (i.e., flexion/extension, radial/ulnar de-viation) to control the teleportation parabola;(c) Study exploring hand based teleportation techniques in realisticnavigation tasks at combinations of varying distances, elevations,and angles - which has not been explored in the context of VRteleportation.2. PAWS - a personalized arm and wrist-based teleportation techniquefor virtual environments.(a) Design and evaluation of varying degrees of personalization todetermine which combination of personalized attributes resultsin most improved user performance;(b) An exploration of different mapping functions to remedy increasedsensitivity due to personalization;71.6. THESIS OVERVIEW3. Design Guidelines for hands-only controller-free teleportation.1.6 Thesis OverviewThis chapter (Chapter 1: Introduction) provides an introduction toteleportation-based locomotion in virtual reality, an overview of the the-sis, the motivations behind this work, the research questions that were ex-plored, and the approach taken to explore them, followed by a discussionof the contributions of this thesis. The next chapter (Chapter 2: RelatedWork) discusses relevant literature, starting with an overview of VR loco-motion and some of the prominent works. This is followed by an overview ofcontroller-free hands-only virtual reality, focusing on gestures, locomotion,and personalization for each user. In the next two chapters, Chapters 3and 4, I present four user studies that design and evaluate controller-freehands-only teleportation techniques - each chapter presents two studies. InChapter 3, the first study explores suitable gestures required for teleporta-tion activities. Based on the results, the second study explores the evaluationof a novel wrist and arm-based hands-only teleportation technique. ThenChapter 4 explores the personalization of wrist and arm-based hands-onlyteleportation, with study 3 exploring varying degrees of physical attributepersonalization and study 4 using sensitivity curves to improve upon it. Inthe last two chapters, I present design guidelines, and future work, and con-clude my thesis. More specifically, in Chapter 5, I interpret key findingsfrom my studies and present design guidelines for future developers andresearchers, along with limitations and future work. Finally, I end with aconclusion in Chapter 6.8Chapter 2Related WorkVirtual Reality (VR) head-mounted displays (HMD) offer users the ex-perience of being immersed in virtual worlds and they give users the abilityto move around freely in these virtual spaces. Moving through a 3D spaceis a common task in VR and is referred to as locomotion in the litera-ture. Some popular techniques of VR locomotion include teleportation, realwalking [30], redirected walking [75], and Walking-in-Place [87]. In Tele-portation, a user points to a destination where they want to go to with apointing device such as a controller and performs a confirmation action (e.g.,pressing a button or releasing a button), then they are immediately movedor “Teleported” to that destination. Real-world walking is done in a largeopen space where walking in the real world is mapped one-to-one in thevirtual world. Redirected walking also allows the user to walk, however, ina limited physical space and their virtual environment is manipulated in acertain way so that they always remain within the boundary of that limitedphysical space. In the Walking-in-Place technique, the user stands in onespot while performing a movement similar to walking to move around inthe virtual space. Besides these techniques, researchers have explored manyother locomotion techniques [18] for locomotion virtual reality, however inmost commercial VR applications, controller-based Teleportation is the pri-mary locomotion method and it has also been used widely in previous work[6, 18, 33].In this thesis, I explore using the hand as a viable controller-free al-ternative for the teleportation technique in Virtual Reality. This work isinspired by prior work that has explored ways to design locomotion, tele-portation, hands-only locomotion, and personalization in VR. I investigatehands-only teleportation since the next-generation of head-mounted displays[5, 89] include hands-only controls, hence warranting further investigationinto this form of input for locomotion as it is a crucial part of VR. I focuson the parabolic pointer teleportation technique - the de-facto standard inmost commercial controller-based applications and used in previous work[6, 18, 33]. I present an overview of the prior work in these related areas.92.1. LOCOMOTION IN VIRTUAL REALITYFigure 2.1: An overview of the relevant locomotion techniques and some oftheir key attributes which are explored in this thesis. PAWS is the techniquedesigned and developed as the final product of this thesis. Empty boxesdenote attributes that have results which are mixed or not applicable.2.1 Locomotion in Virtual Reality2.1.1 OverviewOne of the key problems in Virtual Reality (VR) is that if VR experiencesare to be made available and accessible to all users, a user needs to be ableto perform locomotion tasks in a limited amount of space with high levels ofusability, performance and immersion. Another issue in VR locomotion isVR sickness. VR sickness which is also referred to as “Simulator Sickness”or “Cybersickness”, is the experience of nausea or motion sickness usersexperience in Virtual Reality due to a mismatch between their visual andolfactory systems [24, 51, 73]. To solve many such problems, researchershave explored different locomotion techniques allowing users to navigatelarge spaces in VR environments in a limited physical space [18]. Eventhough these techniques are large in variety and differ from each other inmany ways, they can be grouped based on the type of movement. The twocommon types of locomotion widely explored in prior work grouped by thetype of movement are: continuous and instant.102.1. LOCOMOTION IN VIRTUAL REALITY2.1.2 Continuous LocomotionWith a continuous locomotion technique, users move to their destinationwhile simulating a real-world movement such as walking, flying, or being ina vehicle [18]. The movement in the virtual world is always smooth, withcontinuous steps without any breaks in movement in between. Most contin-uous techniques are known for their high levels of presence and immersion,but their drawback is usually high levels of VR sickness, requirements oflarge spaces and hardware [18]. I discuss some of the relevant continuoustechniques in this section.3D Locomotion and FlyingOne of the earliest locomotion methods for Virtual Reality was intro-duced by Robinett et al. [79], they proposed the “Flying” technique whereusers would use a hand-held input device and use it to orient their direc-tion of travel and “fly” around the virtual world. Here, the users were notrestricted by any physics-based laws and were able to move around in 3Dspace in any direction. Besides a hand-held device, steering mechanismssuch as hand directed (travel in the direction of the users hand) and gaze(travel in the direction the user is looking) [65] have been explored. In amore modern work, Chen et al. [17] explored a flying-based VR locomotionmethod and compared joystick-based controller and head movements forgiving the user steering control of their game character with full 6-degreesof freedom (ability to move in any direction). They performed a user studyincluding realistic settings such as elevation changes and targets in differentangles appearing. Results showed that compared to joystick-based control,the head-controlled steering technique significantly improved user perfor-mance and provided better feelings of presence. Even though head-basedsteering reduced VR sickness compared to the joystick-based control, theystill reported high levels of VR sickness overall.In “Magic Carpet” [63], Medeiros et al. explored flying where a userwould stand on a virtual object when they are flying, called the “carpet”.This carpet acted as a floor reference for the user to stand on and similarto traditional flying, their technique allowed both vertical and horizontaltravel in large 3D environments. They performed two user studies com-paring techniques such as Gaze-based Steering, Hand Steering, Joystick,Walking-in-Place and speed circle (users lean towards the direction of travel)techniques. Study results showed that using hand-based aiming and speedcircle technique performed the best for 3D navigation with the Magic Car-112.1. LOCOMOTION IN VIRTUAL REALITYpet Metaphor. The authors suggested that by providing the user somethingto stand on, they reduced the unnatural feeling of floating in space and alsoprovided a reference for steering (e.g. a car).Body Movement and WalkingOne of the earliest works in VR locomotion using motion and movementwas designed by Fairchild et al. [31]. They explored Lean-Based navigation,where users leaned their body in the direction they wanted to travel in thevirtual world. They suggested two different kinds of locomotion based onleaning: absolute leaning for short distances and relative leaning for largedistances. In absolute leaning the amount of movement was equally mappedfrom the users movements whereas in relative leaning, the movement becamemore and more faster with higher amounts of leaning by the user. Theyreported from a demonstration that Lean-Based navigation was effectivein users who were new to VR for locomotion. Another earlier work by,Slater et al. [84] proposed the Walking-in-Place technique that allowedusers to travel inside the VR environment where they stood in the samespot and moved their feet up and down without moving around in theirphysical environment. They performed two empirical studies evaluatingthe technique and reported the results. Their results showed that walking-in-place provided a higher sense of presence to the user and negated theunrealistic feeling of flying through space when movement was not used.Techniques such as Arm-Swing [67] have used arm movement instead of legsto provide a similar type of VR locomotion in recent times with promisingresults that showed improved performance over Walking-in-Place.Usoh et al. [87] proposed a technique called Real walking, where the par-ticipants were able to walk freely in a 10m × 4m physical environment wheretheir movement was mapped 1:1 to the VR environment. They conducted auser study to evaluate users’ perception of presence where the study resultsshowed that Real walking offered better immersion compared to Walking-in-place technique. They also reported that users had to adjust the virtualspace with a controller when they reached the end of the physical space tocontinue navigating in the virtual world (while reorienting themselves forwalking in the physical world). They concluded that real walking was prac-tical in small (human-scale) spaces, however came with severe limitationssuch as still requiring at least 10m × 4m real world space, manual immersionbreaking reset on reaching the limits of the space and not scaling to largeenvironments.To make unlimited walking possible in a limited physical environment,122.1. LOCOMOTION IN VIRTUAL REALITYRazzaque et al. [75] proposed Redirected walking. This was similar to realwalking where users actually walked in the real world to walk in the virtualworld, however the system used visual perception manipulations to makethe user walk in a circular path even when they thought they were walkingnormally. Even when navigating large virtual worlds, the users implicitlywalked in a small circular path to navigate in the virtual environment whichreduced the requirement of large open spaces. Results from their user studiesshowed that Redirected walking allowed users to turn more naturally com-pared to Real walking when they approached the boundary and also provideda promising locomotion solution for small environments. This technique stillhad some limitations and provided limited amount of travel depending onthe amount of space available. Improving upon this work, Rietzler [78]proposed a modified redirected walking technique, Telewalk, which enabledunlimited walking in a room-scale environment (3m × 3m), whereas theprevious redirected walking methods required larger than room-scale envi-ronments. Since then, there have been many works exploring redirectedwalking in various aspects such as perception [85], application [54], haptics[52], physical space requirements [2].Special HardwareOne of the first usages of “The Omni-directional Treadmill” for VR lo-comotion was explored by Darken et al. [27] where they proposed using aspecial treadmill device that enabled walking and running in any direction.The users movement on the treadmill was tracked and translated into thevirtual world for realistic unlimited locomotion. As this was an early work,their treadmill was a large loud device dependent on military hardware, wasawkward in movements such as side stepping, and required the users to adaptto a certain type of walking. Since then, researchers have worked on improv-ing the omni-directional treadmill and modern versions of omni-directionaltreadmills have been developed attempting to solve some of these issues re-sulting in the current availability of consumer omni-directional treadmills[45]. However, there are still many limitations such as the hardware be-ing expensive, requiring separate setup space, initial setup time each timeof usage and learning-curve of walking on the treadmill [45]. Many otherspecial purpose devices (e.g. hamster ball [61], stepper machine [61]) havebeen explored by researchers, however, they have not become mainstreamor commercially available [18]. Additionally, many of the movement-basedtechniques discussed in section 2.1.2 required special hardware such as mo-tion tracking systems. However, newer consumer VR devices such as the132.1. LOCOMOTION IN VIRTUAL REALITYQuest 2 and Quest 2 Pro [5] came with built in cameras for tracking partsof the human body without any additional hardware.2.1.3 Instant LocomotionIn instant locomotion, users move instantly to their destination with onlyone or very few many steps in between depending on the technique. Themovement from one point to another point in the virtual world is discreteand disconnected. One of the earlier works in teleportation locomotion wasnamed the Jumper Metaphor [7], which introduced a technique where theuser had the capability to select a travel destination by looking at the desireddirection and if they held their gaze for a fixed amount of time(seconds),a “jump” was initiated, triggering a movement of the user to the targetposition. Similar instant movements and their effects have been studied byBowman et al. [9] and Cliburn et al. [23] and their results showed thatinstant locomotion techniques reduced VR sickness with the trade-off ofincreased disorientation. Even though instant locomotion techniques havebeen shown to have some disadvantages such as disorientation and reducedimmersion [10], the primary advantage has been shown to be the lack of anysignificant VR sickness [4, 39] and very minimal physical space requirements,compared to most continuous techniques (section 2.1.2). Consequently, in-stant locomotion techniques such as Teleportation have been widely used inresearch [33] and many mainstream video games such as “Half-Life Alyx”,“No Mans Sky”, “Elder Scrolls V” and “Rec Room” have teleportation asthe default locomotion method.TeleportTeleportation was first mentioned by Mine [65] as a technique for virtualreality locomotion. Since then it has been explored in research primarilyusing hand-held controller buttons and the 3D positioning of the controllerto move the teleportation pointer [11, 62].Teleportation has been an important locomotion technique both in re-search and in the commercial space as it provided several advantages suchas high usability and user preference [18], reduced VR sickness [24, 73] andlimited physical space requirements [34] over other means of VR locomo-tion. Cherni et al. [18] compared 22 locomotion techniques and found thatTeleportation with hand gestures, Teleportation with HMD Controllers andReal Walking were the most preferred locomotion techniques and were highlyrated on presence, ease of use, control-precision, tiredness, and motion sick-142.2. CONTROLLER-FREE HANDS-ONLY VIRTUAL REALITYness. In another work, Rietzler et al. [78] revealed that Teleportation hada lower level of motion sickness and required less time to reach the finaldestination compared to Redirected walking [78]. This was also confirmed in[34], where they revealed that Teleportation caused the least motion sick-ness and provided users with an equal level of spatial information in VR [92]compared to other locomotion techniques. Langbehn et al. [55] comparedGamepad, Teleportation and Redirected walking in a Virtual Environment,and they revealed that Teleportation and Redirected walking were preferredover Gamepad as it caused significant motion sickness. Although Walking-in-Place methods improved the presence of the user, they were found tobe less efficient (i.e., higher task completion time) than other locomotiontechniques based on study results [55].Consecutive work has shown Teleportation to have significantly reducedmotion sickness to the point of being non-existent in most users [24, 73].Though the linear pointer showed benefits in the initial works in teleporta-tion, the curved parabolic pointer has become the most commonly used tele-portation pointing technique for research, commercial and general-purposeVR experiences and games [25, 35, 38, 72, 90]. I discussed some more mod-ern work on teleportation in the controller-free context in section 2.2.4.2.2 Controller-Free Hands-Only Virtual Reality2.2.1 OverviewVarious motion tracking systems have been used as a way to track theusers’ hands for controller-free virtual reality. Most existing works havemostly leveraged markers motion tracking systems for hand tracking andcontroller-free VR interactions. However, many VR devices have now beenreleased with onboard cameras that are able to track a user’s hands and envi-ronment without requiring such motion-tracking systems. In-headset hand-tracking solutions for hand-based interactions have now become a promisingalternative to physical controllers for Virtual Reality.2.2.2 GesturesThe effectiveness of a hand-based interaction in VR has been shown tobe dependent on various factors such as fatigue level, naturalness, gesturespeed and gesture accuracy [3]. Intuitiveness, learnability and ease of usehave also been shown as essential factors [86] for designing an appropriategesture to perform a task. To find suitable gestures for various VR tasks,152.2. CONTROLLER-FREE HANDS-ONLY VIRTUAL REALITYgesture elicitation studies have been performed. Ciss et al. [21] worked withdesigners to elicit 64 mid-air hand gestures for locomotion in VR. Theyevaluated the gestures based on five criteria to design a suitable design agesture set to perform two-handed locomotion. Sampson et al. [81] tookinto account the limitations of Leap Motion sensors and designed a handgesture set for navigation and interaction in VR environments. They sug-gested using gestures distinct from unconscious movement so that they didnot trigger an unintended action (i.e. the Midas touch problem) [81]. Re-searchers explored ways to solve this issue by using a delimiter, which was aspecific user-performed action to define the start and end of a gesture [64]).However, to my best knowledge, there have been no formal studies explor-ing different navigation-related activities such as activation of teleportation(i.e., delimiter), navigation, confirmation of teleportation and cancellation,which are common in locomotion. Consequently, I conducted a design studyexploring suitable gestures for navigation-related activities.2.2.3 LocomotionMine et al. [65] proposed one of the first hand-based works, with tech-niques designed to control the motion direction via users’ hand position andorientation. The authors noted that mapping physical motion (e.g., handmovement) to the virtual movement was one of the most intuitive means oftransportation through a virtual world. Pie et al. [67] showed that the hand-based locomotion technique produced less motion sickness and lower taskload while having a similar level of naturalness and immersion asWalking-in-Place. Researchers also revealed that gesture-based locomotion techniqueswere more intuitive than using physical controllers such as a joystick [50].Chastine et al. [16] investigated a gesture-based navigation techniquewhere users movement in VR depends on the relative distance between theirpalms and a Leap motion device. With a user study, they found that thegesture-based technique performed poorly compared to Keyboard+Mouseand Gamepad. In addition, participants expressed their concerns aboutmaintaining the index finger pointed for a prolonged time. Ferracani etal. [32] designed and compared four controller-free techniques, with twogesture-based locomotion techniques named “Tap” and “Push”. With Tap,users performed a mid-air tap gesture toward their intended direction withtheir index finger. With Push, users moved their hand forward and back-ward towards their intended direction (mimicking the motion of pushing).With both Tap and Push techniques, the gesture started moving the usersmoothly to their intended direction until they performed a stop gesture.162.2. CONTROLLER-FREE HANDS-ONLY VIRTUAL REALITYAuthors compared Tap with other locomotion techniques such as Walking-in-Place [87](where users move their legs up and down in walking motion,yet remaining in the same spot) and Swing [67](where users swing their armsbeside their body similar to jogging). Results revealed that pointing withTap provided higher accuracy, lower fatigue and higher perceived immer-sion than the other techniques. Zhang et al. [94] used the Oculus Rift alongwith a Leap Motion sensor to implement a double-handed gesture-basednavigation technique, allowing users to turn easily while walking. However,compared to the gamepad-based navigation and Portal (i.e., gamepad-basedteleportation), the gesture-based navigation technique did not perform wellwhen considering the mean travel time.2.2.4 TeleportationIn a recent work, Bozgeyikli et al. [11] introduced Point & Teleport inVR, which was an adaptation of the Jumper metaphor. In this technique, amotion tracking system was used to track the users arm (hand and shoul-der). The user pointed in a straight line (i.e., linear pointer) to a destina-tion in VR and waited two seconds to be instantly moved to that location.They conducted two user studies comparing their techniques to other ex-isting techniques, where results showed that their technique was intuitiveand easy to use for locomotion in VR. In a follow-up study, Bozgeyikili etal. [10] compared different locomotion techniques, which included Gamepad,Walking-in-Place, Redirected walking, a Stepper, Trackball and hand gesture-based techniques. Their study results showed that Gamepad and Point &Teleport were the two preferred techniques based on user ratings.Funk et al. [35] redesigned two novel teleportation techniques basedon Point & Teleport while using a curved trajectory and compared it withLinear Teleport and AngleSelect Teleport [10]. They perform a user studycomparing the techniques and their results showed that the curved trajec-tory technique reduced users’ need to reorient themselves but had a highertask completion time. Schfer et al. [83] explored single-handed gestures,two-handed gestures and timeouts for teleportation confirmation along withthe index finger and palm as a source of straight line-based ray. Resultsfrom their user study showed that using the palm as a source along withgestures had a high level of usability and offered ease of use to the users.von Willich et al. [90] explored various foot based techniques to controlthe parabolic teleportation pointer using the foot’s 3D position and appliedpressure on the sole of the foot. They showed that foot-based teleportationwas a viable alternative, however, produced low accuracy and convenience.172.2. CONTROLLER-FREE HANDS-ONLY VIRTUAL REALITYThe authors noted that controller-free teleportation was an important areafor exploration as it allowed users to perform natural hand interactions inVR.Previous works have revealed that controller-free locomotion techniquesallowed users to perform natural hand interactions, were effective, causedless motion-sickness and lower task load than controller-based techniques,while they also provided intuitive, natural and immersive experiences [50,67]. Consequently, I focused on hands-only locomotion and design tech-niques as possible solutions to problems such as user-to-user differences inarm and wrist movement range that arise from going “controller-free”.2.2.5 PersonalizationOverviewArazy et al. [1] defined the term “personalization” as an effective wayto accommodate individuals’ differences into the design of user interfaces.For instance, content recommendation, tailoring user interfaces, or adapt-ing the behaviour of a system to match the user’s needs - thus supportingpersonalization [46, 60, 71]. Calibration has been defined as the process offine-tuning the parameters of a system to produce more accurate or pre-cise results [82]. Both calibration and personalization have been used forimproving user experience by making the system more effective. Personal-ization has usually focused on individualizing the experience for each user,while calibration has commonly focused on optimizing the system’s perfor-mance (usually accuracy or precision) overall. Situating my work in PAWS(chapter 4) between calibration and personalization can be dependent on theinterpretation of the terminology. In PAWS, I involved users by gatheringtheir performance, feedback and preferences, as well as body measures andfound suitable ways to tailor the system to them individually. In PAWS,the technique was adapted to each user to match the user’s needs - thus Ibelieve that this situated PAWS more in personalization.Relevant WorksIn a recent work by Hammond et al. [60], the authors showed that usersprefer personalized pointing techniques for web navigation. More specifi-cally, users preferred a system that adapted pointer movements over timewhile also considering user preferences to overcome any physical limitations.Hourcade et al. [46] showed that adapting the pointing speed according tousers personal abilities improved pointing accuracy and intuitiveness, with182.2. CONTROLLER-FREE HANDS-ONLY VIRTUAL REALITYchildren and adults benefiting the most when navigating the web in 2Dinterfaces. Broll et al. [12] analyzed the requirements for AR applicationsfrom different user groups and found that a single user interaction techniquecannot satisfy every user’s needs. They hence recommended designing per-sonalized interfaces for augmented reality. Martn et al. [59] conducteddesign sessions with older and younger adults with accessibility issues suchas limited hand dexterity. They showed that personalization or adaptationwas critical to prevent certain groups from being disadvantaged by the sys-tem. In “Tailored Virtual Reality” [71], authors presented user-tailored VRexercises for smart physiotherapy and showed that personalized VR appli-cations provide better understanding and motivation for users to continueusing the application. From these previous works, it can be identified thatusers with varying physical attributes require systems to be adapted and per-sonalized for equal usability. With hand-tracking, the differences in physicalattributes and motion range have become more apparent as some users facedisadvantages and require longer times traversing heights or distances. Asa result, I explored solutions for improving hands-only teleportation usingpersonalization, as it would allow every user a faster and more enhancedexperience in the exploration of virtual worlds.19Chapter 3Designing Controller FreeTeleportationPhysical controllers are the primary mode for user interaction with thevirtual world. However, researchers showed that controller-based interactioncould be cumbersome to perform activities in VR [69] such as grabbing,picking up objects, and object manipulation. In contrast, using the handsto perform such activities are more natural [66]. Consequently, prior workexplored controller-free hand-based solutions [11, 47, 50, 83, 94] to navigatefrom one location to another in virtual environments. Their results showedthat bare-hand techniques resulted in less motion-sickness, decreased taskloads than controller-based techniques, and provided intuitive and immersiveexperiences [50, 67].Teleportation is the default locomotion method in most commercialcontroller-based VR applications and has been used widely in previous work[6, 18, 33]. However, an in-depth exploration of hand-based teleportationtechniques remains relatively unexplored. Therefore, in my thesis, I first ex-plore (RQ1): How can hand gestures along with arm and wrist movements,be used instead of controllers to perform realistic navigation tasks using VRteleportation? To explore this research question, I investigate: (i) how userscan utilize hand gestures for teleportation tasks without physical controllersand (ii) how wrist and arm movements can be leveraged to manipulate theparabolic pointer for hands-only teleportation.First, I designed and evaluated WriArm, a novel wrist and arm-basedteleportation technique for VR. I first conduct a design study to exploregesture-based solutions (e.g., finger movement) as alternatives to controllerbuttons for teleportation subactivities. More specifically, I identified foursub-activities, i.e., Activation, pointing, Confirmation, and Cancellation,from controller-based techniques. I then conducted a design study to exploresuitable gesture sets that could substitute for button presses for those sub-activities. The results of study 1 provided me with users’ feedback on thegesture sets. As a next step, I conducted a second study (i.e., user study2) to evaluate user performance, such as task completion time, using the203.1. DESIGN STUDY 1: EXPLORING HAND GESTURESgesture sets and techniques in combination to assess their performance andusability.3.1 Design Study 1: Exploring Hand GesturesThe goal of this design study is to explore hand gestures to act as substi-tutes for controller button presses in various teleportation activities in VR.While exploring teleportation in VR with the standard controller, I observedthe following common teleportation sub-activities:− Activation: refers to the action that initiates the teleportation process.Once an action is performed, the controller emits a ray from the device,indicating that the user can start teleportation in the VR environment.− Pointing: in VR, locomotion is commonly done with virtual ray cast-ing, where the controller controls the origin, destination and orienta-tion of a ray in a similar way to manipulating a laser pointer. Bychanging the controller’s position and orientation, a user can adjustthe destination (i.e., where to teleport).− Confirmation: is the action that causes the user to teleport to the des-tination defined by the ray casting pointer. The user can commonlyissue a confirmation by pressing a button on the controller. Once aconfirmation action is triggered, the user is teleported to the destina-tion.− Cancellation: is the action to deactivate the ray that is initiated duringthe activation process. Once the cancellation is triggered, the user hasto activate the ray again to continue navigating. On a controller,cancellation is commonly performed by pressing a button.Therefore, I first explore hand gestures that can be used for controller-freeteleportation in VR. Hand and finger movements can offer many gestureoptions that can be mapped to teleportation activities. To limit the numberof options, I reviewed prior work [15, 22, 28, 29, 56, 58, 70, 81, 91] focusing onuser elicited hand and finger gestures for mobile, AR and VR interactions.Consequently, I come up with a set of 21 hand and finger gestures thatare commonly reported in prior work. I then elicited designers’ input togain initial insights on gestures suitable for controller-free teleportation. Iam unaware of any prior work that generates a catalog of hand gesturesdesigned explicitly for the above teleportation sub-activities.213.1. DESIGN STUDY 1: EXPLORING HAND GESTURESFigure 3.1: Selected gesture sets for teleportations after the design study223.1. DESIGN STUDY 1: EXPLORING HAND GESTURES3.1.1 ParticipantsIn order to determine suitable gestures for teleportation sub-activities,I first ran a design session. For recruiting participants, I sent invitationsto HCI/Interaction design and media studies-related labs. Eight graduatestudents (ages 21 to 32, 1 Female, mean age 28.13, SD 3.48) who haveexperience with designing and developing AR/VR applications, participatedin the study. I selected this group as they are familiar with design theoryand its processes. None of the participants were aware of the design of thestudy. Due to the surge of the Omicron variant of Covid-19, sessions wereconducted online via Zoom as a precautionary measure. Each session lastedaround 30 minutes.3.1.2 ProcedureInstead of having participants rate each and every option, I let the par-ticipants produce three gesture sets, each set containing four gestures onefor each teleportation sub-activity. They were asked to try different gesturesby themselves first and decide on gestures that they would prefer to use forthe teleportation sub-activities (i.e. activation, pointing, confirmation, andcancellation). Participants were also instructed to add any new gestures ifthey did not find a desired gesture on the slides. I structured the designsession in two phases.In the first phase, I explained the goal of the session, how teleportationsub-activities work and how these activities are commonly performed withthe standard controller. I then presented participants with slides showingthe possible gestures via Zoom. Textual descriptions (e.g., Fist or Pinch)of each of the gestures were also displayed. I instructed them to perform agesture and its possible variants before selecting it as the preferred gesturefor a navigation sub-activity. In the end, each participant provided us witha minimum set of three gesture sets, each containing four gestures one foreach navigation sub-activity. Their gesture preferences were collected usinga Qualtrics form. Each session took approximately 20 minutes to complete.3.1.3 Data CollectionI collected a total of 25 gesture sets from the participants. As I want toevaluate users performance with the gesture sets for teleportation activities,I took further steps to find a small set that can be investigated further with auser study. Researchers with an HCI background analyzed all the submittedresponses from participants to find a small gesture set by (i) removing the233.1. DESIGN STUDY 1: EXPLORING HAND GESTURESrepeated gestures for the same sub-activity, (ii) eliminating the gesturesthat cannot be captured with the VR headsets embedded cameras whileavoiding any detection issues (e.g., occluded fingers) and (iii) avoiding setsthat require significant hand/finger adjustment to switch between navigationsub-activities. For example, flipping the arm and closing the fingers toswitch from pointing to confirmation requires considerable changes in thearm/fingers and are thus ignored. Figure 3.1 shows the five gesture setsselected during the process. In the second phase, I sent these 5 gesture setsto the eight participants who participated in the first phase. I asked themto first perform the set by themselves, then provide their preference of thegesture sets on a 5-point Likert scale and add a justification of their ratingsin a Qualtrics form.3.1.4 ResultsFigure 3.2: Participants preference ratings across the gesture setsFigure 3.2 shows participants preference ratings across the gesture sets. Iconducted a Friedman test where the results showed no significant differenceson ratings across the gesture sets (χ2(4, N = 8) = 4.61, p = 0.33).However, I found that Set 3 (mean 3.50), Set 4 (mean 3.25) and Set 1(mean 3.00) had higher user ratings than Set 2 (mean 2.25) and Set 5 (mean1.75). Participants commented on Set 2 and Set 5, expressing their concernswith these two sets. For instance, one participant commented, Set 5 feels likeit requires a lot of effort and forethought. Another participant mentioned,Set 5 was tough to remember. In addition, one participant commented,[for set 2] it feels like initialization and navigation are switched for what isintuitive. Therefore, I decided to move forward with Set 1, Set 3 and Set 4and explore users’ performance with these sets.243.2. TECHNIQUES FOR CONTROLLING THE TELEPORTATION POINTER3.2 Techniques for Controlling the TeleportationPointerThere are several ways to display the teleportation pointer; the mostcommon is showing the ray as a straight line or a parabola. As the parabolicpointer is the standard in most commercial applications and widely usedin pointing techniques [6, 18, 33] in controller-based methods, I use it fordesigning controller-free hand-based teleportation techniques.For manipulating the position of the parabolic pointer in the VR environ-ment, I considered multiple controller-free input sources. In the exploratoryphase, I examined the gaze, arm, wrist and the tip of the index finger asthe pointer manipulation source. During pilot sessions, I found that consis-tently holding the finger stable in mid-air for precise pointing is difficult dueto jitters. In addition, the gaze pointer caused nausea and motion sickness.Therefore, I excluded these two sources from my exploration.Prior work on hand-based teleportation relies on arm movement [10, 20,50, 94] to point towards the intended teleportation destination. Researchersrevealed that only arm-based solutions are not optimal for teleportation,especially when it requires users to move to a higher elevation (e.g., stairs),and impacts overall users performance [10, 62]. Wrist input can act as acomplementary input to arm input while extending the ability to controlthe angle of the parabolic pointer using the wrist’s angle. In addition, Inoticed that the wrist offered more control for precise pointer movements,while the arm provided rapid and larger pointer movements. Consequently,I decided to explore the combination of both wrist and arm for controllingthe pointer. Finally, I narrowed down my exploration to the following twotechniques:Figure 3.3: (a-b) Arm-based teleportation technique.253.2. TECHNIQUES FOR CONTROLLING THE TELEPORTATION POINTER− Arm: In this technique, the arm’s position (from a reference point) isused for moving the teleportation pointer. To allow users to reach apositive or negative elevation, I use the height of the arm from a baseposition (i.e., the arm in a position with the elbow at a 90◦ angle) tocreate an angle to calculate and draw the parabola. Once the arm isin the base position, I consider the angle as 0◦. As the arm movesup or down from the base position, I increase or decrease the anglevalues. With a pilot study, I find a suitable mapping between thearm height and the angle values, making it possible to reach the mostheights and distances while the arm position is still comfortable. Ifthe arm is lower than the base position, the parabola source is pointeddownwards, allowing for teleportation to low or short destinations.Conversely, if the arm is higher than the base position, the parabolais pointed upwards, allowing for teleportation over elevated or fartherdestinations (see Figure 3.3).− WriArm: In this technique, the arm’s position is only used as the po-sition for the source for the parabolic pointer. Therefore, lifting orlowering the arm is used to control the height of the pointer. Wristhere acts as a secondary input, further extending the ability to con-trol the angle of the parabolic pointer using the wrists angle itself. Iconsider the straight wrist being parallel to the ground as the 0◦ (i.e.,base position). Once users move their wrists upward (i.e., extension),downward (i.e., flexion), left (i.e., radial deviation) or right (i.e., ulnardeviation), I calculate the angle of the wrist from the base position anduse this angle to determine the source angle of the parabolic pointer.With this added angle, comfortable adjustments of the pointer canbe made using the wrist angle, and higher and lower destinations canbe reached without lifting or lowering the arm. In essence, the armcontrols broad pointer movements, and the wrist angle controls theangle of the pointer origin, thus assisting in reaching higher and lowerelevations and precise pointer movements (see Figures 1.1 and 1.2).I calculated the points using the following formula to draw the parabola:y =12at2 + v0t+ y0y = height, t = time in seconds, a = acceleration due to gravity, v0 =initial (starting) velocity, y0 = initial (starting) height. I used Unity’sbuilt in physics engine to set the angle at which the projectile thatrepresents the pointer is initially launched.263.3. USER STUDY 2: EVALUATING USER PERFORMANCE3.3 User Study 2: Evaluating User PerformanceThe goal of this study is twofold: (i) compare my wrist-based naviga-tion technique, i.e., WriArm, to the standard arm-based technique, Arm,and (ii) find a suitable gesture set for teleportation. With knowledge ofthree preferred gesture sets from the design study, I then evaluated thesethree gesture sets as well as the two techniques for teleportation in a VRenvironment.3.3.1 ParticipantsI recruited 12 participants aged between 20 to 41 (M = 27.5, SD = 5.92),of which 2 were female. Participants were recruited through word-of-mouthfrom a local university. 10 out of 12 participants reported that they had priorexperience using Virtual Reality Headsets. I ran the user study in person inearly May 2022 when I saw a return to “normalcy” after Covid-19.3.3.2 ApparatusI used the Oculus Quest 2 as the head-mounted display for my study.Instead of using marker-based or external motion tracking solutions for handtracking, I used headset cameras to track the hand. I used MRTK SDK withOculus SDK for hand tracking. I used Unity 3D (2020.3.33f1) as the primarydevelopment software with C# scripting to design and develop the gesturesets, techniques, and the virtual environment. I used MRTK SDK to trackthe position of key points on users hands, such as knuckles or fingertips, inreal time. I used this information to implement three gesture sets selected inthe design session. An Oculus link cable was used to run the application ona computer during the development phase. However, the studies were con-ducted directly on the Quest 2 headset itself, with the application runningon the headsets, without any cables or computer connections, representingreal-world usage. The data logged during a study session was automaticallytransferred from the Oculus Quest 2 to a Cloud-Firestore database.3.3.3 Tasks and ProcedureOur study involved each participant standing in an empty 3×3 metrespace using a Quest 2 with no controllers. Participants were instructed tostand in the centre of the space and remain there while performing the study.Participants performed a set of teleportation tasks where they were requiredto use a gesture set and a technique to navigate in the VR environment. Each273.3. USER STUDY 2: EVALUATING USER PERFORMANCEteleportation involved participants navigating from their current location tothe next location. The next location was determined based on the following:Distance: Prior work used different teleportation distances for investi-gating teleportation techniques. In recent works such as Skyport [62], theresearchers use 7m, 14m, 21m distances to represent an average, a distantand a very distant point where the last distance (21m) is designed to requiremultiple teleportations. The original point and teleport paper [11], used 2mand 4m distances as their goal was to only evaluate usability. For handgesture-based teleportation, [83] used fixed distances of 10m. As none ofthese studies included heights, for my study, in the pilot phase I found 5.5meters as a suitable average distance, and 11m as a distant point that is atnear the limit of comfortable pointing when height change is also included.Therefore, I included 5.5m and 11m teleportation distances in this study.Note that I did not include a distance that required multiple teleportationsas I was interested in exploring the performance of the techniques with asingle teleportation, and multiple teleportations is simply the repetition ofa single teleportation multiple times.Height : Most of the previous works did not explore height as a factor forteleportation activities. Recently, Matviienko et al. [62] explored heightsas a factor in their study where the teleport destinations were positionedvertically up or down. My study considered height and distance in conjunc-tion, allowing the teleportation destination to be placed in more realisticpositions. I did another pilot study where results showed that users couldcomfortably reach up to 5m height when the destinations are placed at themaximum distance (i.e., 11m). Therefore, I used the heights of 2.5m and5m.In my study, I used teleportation tasks where users were required totraverse on the same plane (heights from 0m to 0m), going up (from 0m to2.5 or 0m to 5m) and going down (from 2.5m to 0m or 5m to 0m). I usedthese heights in combination with horizontal distances (i.e., 5.5m and 11m).Teleportation Angle: Instead of showing the destination points directly infront a users’ view, I placed them at random angles between -45◦ and +45◦.I placed the teleportation destinations to the left or right (i.e., negative andpositive angles) while ensuring that no two consecutive teleportations wereplaced in the same direction to reduce directional bias. I also made surethat users had to move their heads a maximum of 45 degrees to locate thenext teleportation point quickly.I used a red semi-transparent sphere (in Figure 3.3) of 1m in diameterto represent the teleportation location (i.e., target location). I placed anarrow above the sphere pointing down towards it to make the location more283.3. USER STUDY 2: EVALUATING USER PERFORMANCEvisible from a distance. In addition, I added a white 1.5m×1.5m platformto give the user the feeling of standing on a platform instead of floating inspace which is unrealistic and can trigger some users’ fear of heights. Thecombinations of height and distance were randomly selected to display thenext target location while ensuring every combination appeared with equalfrequency and the random angles always went left to right and vice versa.Only one target location was visible to the user at a time, with the nextcheckpoint only appearing after a successful teleportation.Figure 3.4: Teleportation height and distance. (a) The user needs to teleportfrom 0m to 0m height with a distance of 5.5m and (b) from 0m to 5m heightwith a distance of 11m.3.3.4 DesignEach trial involved the participant teleporting from their current loca-tion to the target location. Participants began on an initial flat platformwhere they were asked to use a technique and a gesture set to teleport tothe target location. Participants were instructed to point at the sphere anduse a confirmation gesture to teleport to the target location. When par-ticipants aim the parabolic pointer at the sphere, the color of the sphereand the parabolic pointer change from red to green, indicating that theyare successfully aiming at the target. They can now use the confirmationgesture to teleport to the target location. Assigning confirmation gesturesto the non-target locations was disabled (similar to VR games). Performingthe confirmation gesture triggers audio feedback to users, teleports them to293.3. USER STUDY 2: EVALUATING USER PERFORMANCEthe location and makes the next destination point visible.I used a within-subject design with two independent variables: Techniquewith 2 levels (Arm, WriArm) and Gesture Set with 3 levels (Gesture Set 1,Gesture Set 3, and Gesture Set 4). As mentioned before, Distance andHeight were randomly selected and I used 2 Distances (i.e., 5.5m and 11m)and five variations of Height (from 0m to 0m, from 0m to 2.5m, from 0m to5m, from 2.5m to 0m and from 5m to 0m). Both distances were repeatedfor each height to create 10 unique teleportation destinations. I randomizedthe presentation order of the teleportation destinations to avoid any orderbias. However, I ensured that there were no inconsistencies in heights fortwo consecutive teleportations. For instance, once a user teleported from0m to 5m, the next teleportation always started from 5m (See Figure 3.4).Each destination was repeated eight times generating 80 teleportation tasks(i.e., 80 trials) per Technique × Gesture Set combination. The presentationorder of technique was counterbalanced across participants and Gesture Setwas selected randomly. Otherwise, for a fully counterbalanced presentationorder consisting of two techniques and three gesture sets, I would have hadto recruit a large number of participants. Thus, for practical reasons I onlycounterbalanced on the key factor, i.e., Technique.Each participant was first given a brief introduction to the study, whichprovided an explanation of the basic concepts of locomotion, teleportationand the hand gestures. I then explained the first Technique × Gesture Setcombination assigned to the participants and provided a set of practice trials(i.e., 20 trials) with the assigned combination. After finishing the practicetrials, they were given 80 trials with the combination. I provided themwith a 1-minute break after 40 trials to avoid any substantial arm fatigue.After completing the trials (i.e., 80 trials), they were provided with a 3-minute break. I repeated this process with the next Technique × Gesture Setcombination until they had finished all the combinations. Each participantcompleted 2 Technique × 3 Gesture Set × 10 Distance-Height Combination× 8 repetitions = 480 trials. I arrived at this number by running a pilotstudy, where I ensured that the entire study could be completed within areasonable time (i.e., 1 hour), without hand and arm fatigue, while collectinga decent data set. Once they were done, participants were asked to fillout a questionnaire to collect their demographic information and feedbackon the Technique and Gesture Set. In addition, I used the Virtual realitysickness questionnaire (VRSQ) [51] to evaluate the techniques. I collectedfeedback from the participants using the questionnaires after finishing all thecombinations allowing participants to compare and contrast the techniqueswhich is used in many prior works [41, 42, 68] but adapted to my work.303.3. USER STUDY 2: EVALUATING USER PERFORMANCENote that each session lasted approximately 1 hour.3.3.5 ResultsFigure 3.5: Study results. Mean trial time (a) across the techniques and ges-ture sets. Results showed that Technique had significant effects on trial timewhere users were faster with WriArm than Arm as indicated by asterisks(*). I observed that Gesture Set had significant effects on trial time whereusers were faster with gesture set 4 than with gesture set 1 and gesture set3. Mean trial time for (b) distance and height (in seconds). Users’ providedfeedback on their preference, fatigue, speed, accuracy and easiness on a 5-point Likert scale across (c) three gesture sets and (d) two techniques. Iobserved that users’ ratings for WriArm were significantly higher than Armon user preference and easiness as indicated by asterisks.I used trial time as the dependent variable in my study, where trial time isthe time taken to teleport from a location to the destination. In addition, thequestionnaire provided subjective measures of the participants preferences,perceived speed, accuracy, fatigue and ease of use on each gesture set andtechnique, along with the VRSQ providing insight into any motion sicknessor simulator sickness caused by the techniques.313.3. USER STUDY 2: EVALUATING USER PERFORMANCEFigure 3.6: Study results. Users’ provided feedback on their preference,fatigue, speed, accuracy and easiness on a 5-point Likert scale across (a)three gesture sets and (b) two techniques. I observed that users’ ratings forWriArm were significantly higher than Arm on user preference and easiness,as indicated by asterisks.Trial timeI used a two-way repeated measures ANOVA (Analysis of variance) andpost-hoc pairwise comparisons to analyze the mean trial time. Resultsshowed that Technique had significant effects on checkpoint time (F1,11 =6.42, p < 0.05, η2 = 0.37)1. Figure 3.5a shows the mean trial time (inseconds) across Technique × Gesture Set. Results showed that users weresignificantly faster with WriArm (mean 2.35s) than Arm (mean 2.59s).Results also revealed that Gesture Set had significant effects on trial time(F2,22 = 4.69, p < 0.05, η2 = 0.30). Post-hoc pairwise comparisons betweengesture sets showed that users were significantly faster with gesture set 4(mean 2.38s) than with gesture set 1 (mean 2.64s) and gesture set 3 (mean2.39s). There were no other pairwise statistically significant differences.The mean trial time (in seconds) across Technique × Height are shownin Figure 3.5b. In general, users were faster with WriArm than Arm whentraversing straight, going up or down, except for going from 0m to 5m. Iobserved that Arm (mean 2.52s) and WriArm (mean 2.52s) had compara-1(FDOF,CR = F , p < PV , η2 = EF ), where DOF is the degree of freedom, CR is thecorresponding error (N-degree of freedom), PV is p value denoting likelihood of observationbeing non-significant with lower being better, and EF is the effect size which shows themagnitude of difference.323.3. USER STUDY 2: EVALUATING USER PERFORMANCEble performance when going up from 0m to 5m. I also found that bothtechniques required more time when going down from 5m to 0m. I believethat these are due to the maximum height (i.e., 5m) used in the study. Inaddition, I did not find any significant differences in the VRSQ total scoreaccording to the techniques. In the subjective feedback, participants did notreport any issues related to motion sickness.Participant FeedbackI collected users feedback on their preference, fatigue, speed, accuracyand easiness on a 5-point Likert scale for Gesture Set and Technique. Figure3.6a shows the median ratings for the three gesture sets. As the user feedbackwas taken on a Likert scale which produced ordinal non-parametric data,I used a Friedman test to compare the three gesture sets. The Friedmantest found no significant difference among the gesture sets (χ2(2, N = 12) =5.91, p = 0.05)2. I also received participants’ subjective feedback on allthree gesture sets with positive comments on gesture set 3 such as, it (i.e.,gesture set 3) “...was intuitive and caused less fatigue” [P3], “... seemedbetter for selections” [P7]. In addition, one participant commented, “I foundGesture Set 1 to be surprisingly intuitive when using WriArm. However,Gesture Set 4 was also intuitive and caused less fatigue than Gesture Set1” [P4]. Participants also indicated more fatigue with Gesture Set 1 - “Theconfirmation gesture of Gesture Set 1 was easy to perform at first. However,ongoing use of that gesture was causing my hands to become a little sore.”[P2]Further, as there are two techniques (two paired groups), I conductedWilcoxon Signed Ranked Tests to analyze users feedback on Technique. Iuse Cohen’s interpretation of effect size: 0.1: small, 0.3: medium, and >0.5:large effect. Figure 3.6b shows the median ratings for the two techniques.Results showed that WriArm, with a median rating of 5, was significantlydifferent from Arm (median 3) on users preference (z = −2.38, p < 0.05, r =0.69)3 and easiness (z = −2.31, p < 0.05, r = 0.67). There were no otherpairwise statistically significant differences. However, I observed favorablemean user ratings with WriArm for all other categories fatigue: WriArm2.92 vs. Arm 4.17 where a lower value indicates less arm fatigue, speed:2(χ2(DOF,N = N) = C, p = PV ), where DOF is the degree of freedom, N is samplesize or the number of participants, C is the the-square statistic value, and PV is thep-value.3(z = Z, p < PV, r = EF ), where Z is the result, PV is the p-value and EF is the effectsize.333.4. DISCUSSIONWriArm 3.92 vs. Arm 3.50 where a higher value suggests a faster techniqueand accuracy: WriArm 3.83 vs. Arm 3.42 where a higher value indicates amore accurate technique. In addition, participants felt that WriArm is morenatural, more intuitive and thus is an overall preferred technique.3.4 DiscussionRQ1: How can hand gestures along with arm and wrist movements beused instead of controllers to perform realistic navigation tasks using VRteleportation?The results of the two studies show that WriArm, where combined armand wrist movement is used to control the teleportation parabola along withgestures for each activity can be used to perform realistic navigation tasksfor VR teleportation.The WriArm technique, which combines both arm movement and wristangle for pointer control, resulted in significantly faster performance thanthe Arm technique. Users also performed better on Gesture Set 4, which I at-tribute to the simplicity and ease of the pointing and confirmation gestures,as they are the most frequently performed gestures. The difference in point-ing and confirmation of Gesture Set 1 and 4 are only in the orientation of thepalm of the hand, which indicates that having the palm facing up is morenatural and faster for mid-air pointing. The subjective feedback showed thatparticipants preferred the WriArm technique and found it easier and moreintuitive. Prior research has reported arm fatigue and ’gorilla arm’-issuesrelated to arm-based interactions [43]. Since WriArm involves arm move-ments, it also causes arm fatigue. However, as the parabolic pointer can becontrolled with subtle wrist movement, it helps minimise concerns relatedto arm fatigue. Consequently, I observed that the WriArm technique causedless fatigue than Arm, on average, even though no statistically significantdifference was found.34Chapter 4Personalizing Hand-BasedTeleportationIn controller-based teleportation, every user uses a handheld controllerwith the same teleportation capabilities. However, in hands-only teleporta-tion, the capability becomes dependent on users physical attributes, such astheir height or arm length. Therefore, the differences in physical attributesand motion range (between different users) become more apparent as someusers face disadvantages, especially when they need to move their hands toheights or distances further than others. In study 2, I designed and de-veloped WriArm, a novel hands-only teleportation technique for VR. Eventhough the technique provided a fast and usable experience to the user, itwas a generalized technique that did not consider the differences in physi-cal attributes between users. Previous work has shown that personalizingtechniques to users’ attributes can result in improved, more intuitive experi-ences [46] and is preferred by users [60]. These led me to the second researchquestion:RQ2: How can hands-only teleportation be personalized to a combination ofusers’ physical attribute(s) to provide an improved teleportation experiencefor all users?To explore this research question, I investigated: (i) How different levelsof personalization on users arm and wrist movements can be used to improvetheir bare-hand teleportation performance and (ii) How different sensitivityfunctions can be mapped to certain motor movements to improve upon thepersonalized technique.I first conducted a study exploring personalized teleportation techniqueswhile varying the degree of personalization. In a follow-up study, I exploredthe use of sensitivity curves as a remedy to reduced precision and motorstability issues that arise from personalization.354.1. STUDY 3: EXPLORING DIFFERENT LEVELS OF PERSONALIZATION ON TELEPORTATION4.1 Study 3: Exploring Different Levels ofPersonalization on TeleportationThe goal of this study is to explore whether there are certain person-alized combinations of physical attributes that provide more benefits thanothers. There are three primary physical attributes connected to hands-only teleportation: arm vertical movement range, arm horizontal movementrange, and wrist movement range. These attributes can be personalized toeach user in varying combinations. In this study, I investigate and com-pare no-personalization, partial personalization (i.e., only arm horizontaland vertical range), and full personalization (i.e., both wrist and arm rangespersonalization).4.1.1 Hands-Only Parabola Pointer ControlUsers should be able to manipulate four components to fully controla parabolic pointer: the origin, the direction, the launch angle, and thedistance. These components can be controlled with solely hand- and wrist-based movements. The Origin and Direction and Launch Angle of my tech-nique are similar to Point and Teleport [11] and WriArm [19].− 4.1.2 Origin and Direction(Figure 4.1a): Intrinsic to mid-air pointing techniques, the parabolicray starts from the user’ hand. Casting direction mainly relies on armand wrist orientation [10, 19, 20, 50, 94]. The arm provides coarse,rapid and large pointer movements, and the wrist offers more controlfor precise pointer movements.− Launch angle (Figure 4.1b): The parabola’s launch angle is fully con-trolled by the wrist’s upward/extension and downward/flexion move-ments.− Distance (Figure 4.1c): The distance traveled by the parabola can becomputed from the force a projectile would be launched from the originon a given launch angle. I introduce “force-control”, which allowsusers to operate on the parabolic end-point distance by modifying the“force” used to compute the parabola. Users decrease or increase thedistance of the parabola’s end-point by retracting or extending theirarm respectively. Allowing users to control the force of the parabola364.1. STUDY 3: EXPLORING DIFFERENT LEVELS OF PERSONALIZATION ON TELEPORTATIONFigure 4.1: (a) A user can control the ray origin and direction via armand wrist orientation. (b) Launch angle is controlled by the wrist’s up-ward/extension and downward/flexion movements. (c) The user can reacha further distance via arm extension and retraction movements, and (d) bymoving their hand vertically up or down.374.1. STUDY 3: EXPLORING DIFFERENT LEVELS OF PERSONALIZATION ON TELEPORTATION- and hence the traveled distance - is similar to Skyport (for mid-airtargets) [62]. I included this feature as I saw in my pilot that along withwrist movements, force-control allows users to avoid going to extremewrist angles for difficult destinations by increasing or decreasing theforce.However, the distances and heights users can reach with teleportationare different for users with different arm lengths, flexibility of arm move-ment (extension and vertical ranges), and/or flexibility of wrist movement.To provide a similar experience to every user and remove this bias, I nextdiscuss the need to personalize the interaction input based on users physicalattributes. This would ensure that all users can have similar teleportationexperiences regardless of their physical attributes. For example, if a user canonly tilt their wrist to a minimum of -50◦ and a maximum of 50◦ whereasnavigating the environment requires reaching a minimum and a maximumof -75◦ and 75◦, then their [-50, +50] range can be mapped to the [-75, +75]range in the technique.4.1.3 Personalizing Parabola ControlsWith arm and wrist movements, attributes that can be personalized are:the arm extension and retraction range to manipulate the distance via the“force-control” mechanism (Adist), the arm vertical movement range to con-trol the origin (Aori), and the wrist upward/extension and downward/flexionrange to control the launch angle (Wangle). However, it is not clear if allthese attributes warrant a personalized counter-part, or only a combinationof them. It is possible to have the following attribute combinations:− One attribute personalization (Adist, Aori, or Wangle),− Two attributes personalization (Adist +Aori, Adist +Wangle, or Aori +Wangle),− all three attributes personalization (Adist +Aori +Wangle).To reduce the number of combinations, I ran a pilot study to excludeoptions which did not bring any noticeable improvement. As a result, Inarrow down my exploration to three candidates: the baseline (standardarm and wrist movements), baseline + Personalized Adist (PAdist), andfully Personalized Adist +Aori +Wangle (PAW).384.1. STUDY 3: EXPLORING DIFFERENT LEVELS OF PERSONALIZATION ON TELEPORTATIONBaselineThe baseline is set from generic users who can reach targets inside a12m3 box. The origin and the direction of the parabola follow a 1:1(absolute) mapping with the hand’s location and orientation. The standardcomfortable arm vertical range - without personalization - is set to [-0.4 m,+0.5 m] from the headset. The arm flexion/extension movements controlthe distance, i.e., the force at which the parabola is ejected (originallyconstant, F = 12 m/s). The standard comfortable arm flexion/extensionrange - without personalization - is set to [0.11 m, 0.6 m], correspondingto a force in the range F = [1, 12]. The wrist angle is used to controlthe parabola launch angle. When the wrist is parallel to the ground, theparabola is launched at 0◦ (i.e., base position). The angle of the wrist iscalculated as the angle of the wrist from this base position. The user canmove their wrists upward (i.e., extension), and downward (i.e., flexion) tocontrol the launch angle. The standard comfortable wrist flexion/extensionrange - without personalization - is set to [-55◦, +70◦].Personalized Arm (PAdist)The horizontal and vertical movement range of the arm also has an ef-fect on the parabola’s origin and direction and the force-control mechanism,resulting in different minimum and maximum parabola distances for differ-ent users. To personalize these attributes, before using the system, usersextend and retract their arm to comfortable maximum and minimum dis-tances. The system records these values and then adjusts the force between[1,12] with their comfortable range to compensate for the differences frombaseline.Fully Personalized Arm and Wrist (PAW)Before using the system, users aim towards high and low destinationsfor the system to record their maximum and minimum wrist angle andarm vertical ranges. Similarly, users also extend and retract their arm tocomfortable maximum and minimum distances. The system then considersthese three comfortable ranges instead of the three standard ranges used bythe baseline.The baseline parameters need to reflect users with average height andarm length as they are correlated [77] and strongly influence users’ tele-portation performances. The comfortable standard values were estimatedby taking averages from multiple people (N=3) around the average male394.1. STUDY 3: EXPLORING DIFFERENT LEVELS OF PERSONALIZATION ON TELEPORTATIONheight (178 cm) for the local country [26] who were able to navigate theenvironment comfortably. For all techniques, to improve the usability of theparabolic pointer, I use Kalman filters to increase the pointer stability. Ialso add a last-second spike compensation when performing the confirmationgesture to avoid any instability from unintended trigger movements.4.1.4 ParticipantsI recruited 12 right-handed participants (8 male) with a height of 165cm and below (mean height = 162.5, SD = 3.5), ages 19 to 35 (mean age =24.3, SD = 4.1). The system recorded an average arm vertical range from-0.40m (SD = 0.09) to 0.32m (SD = 0.14) and an average arm horizontalrange form 0.19m (SD = 0.37) to 0.51m (SD = 0.06). The baseline usersranges are reported in section 4.1.3 and these values are both reported froma reference point of the headset as the camera-based hand tracking setupon the Quest 2 does not support tracking the shoulder. Participants wererecruited using on-campus flyers and word-of-mouth, and received $15 asan honorarium for participating in the study. Eight of the 12 participantshad prior experience using VR. All eight participants were familiar withcontroller-based teleportation.4.1.5 ApparatusI used the Oculus Quest 2 VR headset for its built-in cameras and theOculus Hand Tracking SDK for hand tracking instead of marker-based orexternal motion tracking solutions. Unity 3D (2020.3.33f1) was used forthe primary development software along with C# scripting to design anddevelop the techniques and the virtual environment. I used the MRTK SDKto access real-time tracking data on key points on users’ hands such as theirpalm and finger joints. Oculus Wireless Link was used for rapid developmenton a computer with RTX 3070 GPU. Note that the user study was conductedsolely on the Quest 2 headset itself, without any connections to a computer,representing real-world usage. The data logged during study sessions wereautomatically transferred from the headset to a Cloud-Firestore database.4.1.6 Experimental Task, Procedure and DesignTaskParticipants performed teleportations to a set of destinations for eachtechnique as shown in Figure 4.2. For my controlled in-lab evaluation, I404.1. STUDY 3: EXPLORING DIFFERENT LEVELS OF PERSONALIZATION ON TELEPORTATIONbound teleportation possibilities to (i) platforms via (ii) a single move.− Platforms: this choice allows us to precisely control each teleportationdistance and elevation. Indeed, floating in mid-air is not standard inVR applications without flying abilities [18]. Platforms fit a metaphorof terrain-based elevation changes while limiting navigation options tomy pre-determined experimental conditions (distance, height).− Single move: this choice also allows us to precisely control experimen-tal conditions in order to generalize conclusions. Indeed, it would notbe possible to conclude on far teleportations if users chose to per-form multiple close moves for instance. Enforcing one teleportationFigure 4.2: Teleportation with Height variation and Distance factors. (a) Auser controls the pointer with arm and wrist movements (Height variation:from 0m to 5 m and Distance: 10.5 m). (b) The color of the parabolic pointerand the sphere changes from red to green once participants successfully aimthe pointer at the sphere (Height variation: from 0 m to 2.5 m and Distance:7.0 m). (c) The user is teleported to the target platform once they issue ahand gesture for selection (i.e., closing their index finger) and looks down tothe next target location. (Height variation: from 5 m to 0 m and Distance:3.5 m)414.1. STUDY 3: EXPLORING DIFFERENT LEVELS OF PERSONALIZATION ON TELEPORTATIONper trial provides the option to cover all conditions while still allow-ing extrapolations of real conditions (e.g., if users prefer to performseveral teleportations for far away targets, I have data for close dis-tances). Additionally, this reduces participants disorientation duringthe experiment [11, 35].Each destination was marked with a red semi-transparent sphere of 0.5meter diameter placed on top of a 1-meter square meter platform. A down-ward arrow was placed above the sphere to facilitate the target search inthe VR environment. Only one target destination (i.e., platform, arrow andthe sphere) was visible to the user (Figure 4.2a) without any occlusion. Thecolor of the sphere and the parabolic pointer change from red to green par-ticipants aim the pointer at the sphere, visually showing that are correctlyaiming at the target (Figure 4.2b). Participants can use a hand gesture fromthe MRTK SDK [80] (i.e., closing their index finger) to teleport to the tar-get platform. Note that issuing confirmation gestures to a non-platform wasdisabled during the study and recorded as an error, similar strategies areused to restrict users from teleporting to out-of-bound areas in VR games[88]. Once participants issued a confirmation gesture to the target sphere,audio feedback was provided indicating successful teleportation. The nextdestination appeared only after a successful teleportation (Figure 4.2b). Insummary, each teleportation required finding the next destination target,aiming at it, and confirming the teleportation. I define the set of desti-nations by considering two main aspects: distance from users, and heightvariation from their current elevation. The angle at which the target ap-peared was randomized between -45◦ and +45◦, alternating between positiveand negative angles for every target to reduce directional bias and ensurethat no two consecutive teleportations were placed in the same direction.− Distance: Previous work focusing on 3D teleportation used differentdistance ranges, such as 7 m and 14 m [62], 10 m [83], or 2 m and4 m [35]. Note that my task is different since I also vary elevation,which has not been investigated yet in combination. I hence ran apilot study where I observed that users could teleport up to a 10.5 mdistance when the targets were elevated. Therefore, I included 3.5 m,7.0 m and 10.5 m as teleportation distances, which covers the range[3.5 m, 11.6 m] when considering both target distance and elevation(i.e., leading to diagonals longer than the distance alone).− Height variation: I define height variation as the height differencebetween the current user position and the target location. This varia-424.1. STUDY 3: EXPLORING DIFFERENT LEVELS OF PERSONALIZATION ON TELEPORTATIONtion can be positive (target on a higher level than the user) or negative(target on a lower level than the user). Matviienko et al. [62] recentlyconsidered vertical heights without varying distances: destination tar-gets were positioned only vertically (up or down). In my study, Iconsider the combination of height variation and distance similar toWriArm [19], leading to more realistic destination placements whileensuring fully controlled settings. Our initial pilot study showed that,with my 10.5 m maximum distance, an elevation of 5 m is a com-fortable maximum height difference for a maximal diagonal distanceof 11.6 m. I hence consider elevations of 2.5 m and 5 m, leading toplanar trajectories (from 0 m to 0 m), upward trajectories (from 0 mto 2.5 m or 0 m to 5 m), and downward trajectories (from 2.5 m to 0m or 5 m to 0 m) for height variation.ProcedureDuring the study, each participant wore the Quest 2 headset withoutcontrollers, and stood in an empty 2×2 meter space. Participants startedwith a brief introduction to VR, locomotion, teleportation, and the studyitself including explanations of each technique. With each technique, par-ticipants first performed 30 practice trials. Then they performed two blocksof 45 trials (i.e., 3 Distance × 5 Height variation × 3 repetitions), with a2-minute break in-between blocks to avoid arm fatigue. After completingthe 90 trials, I asked participants to remove the headset to take a seated3-minute break. During each break, I asked the participant for their per-ceived VR sickness on 20 point scale [62]. I repeated this process with thenext Technique for all three techniques. Each participant completed 3 Tech-niques × 3 Distance × 5 Height variation × 3 repetitions × 3 blocks foreach technique = 270 trials. After the experiment, participants filled outa questionnaire to collect demographic information and feedback on eachTechnique. Collecting feedback after finishing all the combinations allowedparticipants to compare and contrast the techniques [41, 42, 68]. Each ses-sion lasted between 45 minutes and 1 hour.DesignI used a within-subjects design with Technique as the primary inde-pendent variable with 3 levels (Baseline, Personalized Arm Force Control(PAdist), and Personalized Arm and Wrist (PAW)). The order of Techniquewas counter-balanced across participants using a balanced Latin square. I434.1. STUDY 3: EXPLORING DIFFERENT LEVELS OF PERSONALIZATION ON TELEPORTATIONalso consider Distance (3.5 m, 7.5 m,11.5 m) and Height variation (from 0m to 0 m, from 0 m to 2.5 m, from 0 m to 5 m, from 2.5 m to 0 m andfrom 5 m to 0 m). Distance and Height were presented randomly for eachtechnique.The primary measures computed from logs were Trial Time and ErrorRate. Trial Time is the time taken to teleport to a destination. Error Rateis the amount of times the user tried to teleport while not correctly selectingthe destination. I also collected subjective measures via questionnaires. Insummary, I had: 3 techniques × 3 Distances × 5 Height × 6 repetitions =270 trials per participant.4.1.7 ResultsFigure 4.3: Mean (a) trial time and (b) error rate across the techniques.Error bars show the 95% CIs.Trial timeFigure 4.3 (a) shows the mean trial times across the techniques. I use re-peated measures ANOVA and post-hoc pairwise comparisons to analyze themean trial time. Technique has a significant effect (F2,22 = 7.70, p < 0.01,η2 = 0.41) (Figure 4.3a).4 Users are significantly faster with PAW (mean4CIs refer to Confidence intervals which represents the percentage probability of aresult being in a certain range for a certain proportion of times.444.1. STUDY 3: EXPLORING DIFFERENT LEVELS OF PERSONALIZATION ON TELEPORTATION2.56s, Standard Error: SE 0.13) than PAdist (mean 2.74s, SE 0.13) andBaseline (mean 2.85s, SE 0.10). Results also reveal that Distance has sig-nificant effects on trial time (F2,22 = 38.30, p < 0.001, η2 = 0.77): Usersare significantly slower with targets located at 10.5 m (mean 3.05s, SE 0.13)than with targets located at 7.0 m (mean 2.60s, SE 0.11) and 3.5 m (mean2.50s, SE 0.11) from the user. I also observe a significant effect for Heightvariation on trial time (F4,44 = 38.72, p < 0.001, η2 = 0.78). Post-hoc pair-wise comparisons between Height variation show that users are significantlyslower for navigating to extreme upward trajectories (i.e., from 0 m to 5 m),with a mean trial time of 3.40s (SE 0.14), than when teleporting to otherheight variations. I also observe similar results for extreme downward tra-jectories: going from 5 m to 0 m (mean 2.77s, SE 0.13) is significantly slowerthan other height variations except for the upward height variation from 0m to 2.5 m. Results also reveal that navigating from 0 m to 2.5 m (mean2.65s, SE 0.12) is significantly slower than going to a planar trajectory (from0 m to 0 m, mean 2.38, SE 0.10).Error RateI use Friedman tests with Wilcoxon tests for post-hoc pairwise com-parisons to analyze error rates (Figure 4.3b). The overall error rate is15.57%. There is no significant difference in error rates regarding Tech-nique (χ2(2, N = 12) = 2.68, p = 0.26) and Height variation (χ2(4, N =12) = 8.99, p = 0.06). Distance has a significant effect on error rate(χ2(2, N = 12) = 12.81, p < 0.05). Pairwise comparisons show that closedistances (mean 12%) lead to significantly fewer errors than mid (mean 17%)(z = −2.35, p < .01) and far distances (mean 18%) (z = −2.93, p < .01).Preference Scores and Subjective FeedbackI collected users feedback on their preference, fatigue, speed, accuracyand easiness on a 5-point Likert scale for each technique. I perform myanalysis with Wilcoxon Signed Ranked Tests. Figure 4.4 shows the medianratings for Technique. I observe that Technique has a significant effect onusers’ preference (χ2(2, N = 12) = 12.05, p < 0.01). PAW and PAdist, bothwith a median rating of 4, are significantly different from the baseline (me-dian 2.5) on users’ preference (z = −2.44, p < 0.05 and z = −3.17, p < 0.01,respectively). There is no other pairwise statistically significant differences.However, I observed favorable mean user ratings with PAW and PAdist forall other categories: fatigue (PAW 1.58 vs. PAdist 2.08 vs. baseline 2.25454.1. STUDY 3: EXPLORING DIFFERENT LEVELS OF PERSONALIZATION ON TELEPORTATIONwhere a lower value indicates less arm fatigue), speed (PAW 4.00 vs. PAdist4.08 vs. baseline 3.08 where a higher value suggests a faster technique),accuracy (PAW 4.25 vs. PAdist 4.17 vs. baseline 3.50 where a higher valueindicates a more accurate technique) and easiness (PAW 4.25 vs. PAdist4.17 vs. baseline 3.67 where a higher value indicates an easier technique).No notable VR sickness was reported by participants for any of the tech-niques. Participants also provided positive feedback on both personalizedtechniques, PAW and PAdist. For instance, participants commented thatboth “PAW and PFC performed better than the baseline” [P11], and thatthey felt “more comfortable” when using PAW [P5, P7]. However, they ex-pressed that destinations were challenging to reach due to the sensitivity ofthe pointer. Thus, I next explore different sensitivity functions allowing amore fine-grained pointer control.4.1.8 DiscussionUsers are significantly faster with full personalization, when all three at-tributes are personalized (i.e., PAWS) than partial with personalization (i.e.,PAdist) and with no personalization (i.e., baseline). This is also reflectedin users’ subjective ratings revealing preference for personalized techniquesPAWS and PAdist. This shows that PAWS helps users with limited armmovements and wrist flexion/extension abilities.Figure 4.4: (a) Users ratings on baseline, PAdist, and PAW based on theirpreference, fatigue, speed, accuracy and easiness.464.2. STUDY 4: EXPLORING SENSITIVITY FUNCTIONSResults also indicate that reaching distant and elevated targets is moredifficult than in other locations. This is primarily due to distant and el-evated targets while requiring users to reach their maximum comfortable(Adist, Aori, andWangle) ranges. These maximum ranges offer less control andstability, which is also explained by the relatively high error rates for thesedistant targets. This warrants further investigation to explore solutions tocounteract the reduced control and stability for these target locations.4.2 Study 4: Exploring Sensitivity FunctionsStudy 3 explored personalizing the parabola controls based on individualphysical attributes. Using personalized arm and wrist movement ranges ef-fectively allowed a user to teleport faster than no personalization. However,I noticed that there was a performance degradation for difficult-to-reachtargets located to a far and highly elevated platform. I observed that thesedifficult to reach destinations usually required large arm extension and wristangles, which create body postures inadequate for precise and stable move-ments. Users with a lower range of motion for one or more attributes facean increased sensitivity as a trade-off. This is intuitive as controlling a largemovement range using a smaller available range will result in some lossof fine-grained control. Previous work has shown that different sensitivitycurves [74] can provide additional benefits for such motor movements (e.g.,wrist movement).In this follow-up study, I evaluate the effectiveness of mapping functionsthat consider the associations between motor movements and sensitivity asa remedy to the sensitivity trade-offs of personalization. I explore severalsensitivity functions and their combinations tailored to the PAW technique.Recall that PAW uses three movement ranges that can be personalized:the arm extension and retraction range to manipulate the distance via the“force-control” mechanism (Adist), the arm vertical movement range to con-trol the origin (Aori), and the wrist upward/extension and downward/flexionrange to control the launch angle (Wangle). For each of these ranges, the usermight have more or less control and stability over their movements in thebeginning of the range, the middle of the range, or the end of the range.I hence explore suitable sensitivity functions for each movement range. Toevaluate their performance, especially for difficult to reach destinations, Irun a final user study with the most promising curve combinations and test ifusing sensitivity curves provide any benefit in hands-only VR teleportationwith PAW.474.2. STUDY 4: EXPLORING SENSITIVITY FUNCTIONS4.2.1 Sensitivity FunctionsBeside linear mapping (study 1), I consider two other candidates andtheir inverse: quadratic (bell) and sigmoid.− The quadratic function creates a mapping with a fine-grained control(less stable) control in the middle range and coarse control (more sta-ble) controls near the extremes. On the opposite, the inverse quadraticfunction leads to a coarse control in the middle range and fine-grainedcontrols toward the extremes. Both the quadratic function and its in-verse are symmetric, i.e. swapping low and high values would lead tothe same results.− The sigmoid function creates a mapping with fine-grained control(less stable) for low values, and with coarse control (more stable) forthe higher range. On the opposite, the inverse sigmoid function createsa mirrored mapping. Thus, the sigmoid functions are asymmetric.4.2.2 Association Between Motor Movements andSensitivity FunctionsArm MovementsTeleporting to destinations that are especially higher, lower or distantrequire far arm horizontal extension (Adist) and high arm vertical movement(Aori). Previous work by Ramos et al. [43] has shown that when the armis higher or farther from its resting position near the body, movements takemore effort and torque whereas center position is optimal for interaction.I hypothesize that this also applies in my context with users having lessstability and control when the arm is extended far or high up. This issupported by my observations during the first study: when the arm was closeto the body, users rested their elbows on their torso to enhance their armstability and control. However, when the arm is extended or higher up, usersdo not have this extra support. Consequently, I consider arm movementsas asymmetric and exclude symmetric functions (i.e., quadratic). I henceexplore the two sigmoid and inverse-sigmoid functions with arm as shownin Figure 4.5a.Wrist MovementTeleporting to-and-from higher destinations requires large wrist upward/extensionand downward/flexion angles (Wangle). As flexibility and stability of the484.2. STUDY 4: EXPLORING SENSITIVITY FUNCTIONSwrist can differ based on the angle of the wrist, some positions may beharder to hold stable. I observed in my previous study that for high desti-nations, users typically perform very steep wrist angles. Previous work [74]showed that symmetric quadratic sensitivity curves work the best for wristmovement as the middle range ofWangle is more steady than extreme angles.I hence use a symmetric quadratic sensitivity curve for the wrist movement,so that extreme angles benefit from a more stable (coarse) control than themiddle range (Figure 4.6c).4.2.3 Evaluated TechniquesFigure 4.5: (a) The sensitivity curves for each motor movement. Appear-ing left to right in sequence: SSQ (Sigmoid-Sigmoid-Quadratic), IIQ (In-verse Sigmoid-Inverse Sigmoid-Quadratic), SIQ (Sigmoid-Inverse Sigmoid-Quadratic) and ISQ (Inverse Sigmoid-Sigmoid-Quadratic).Mapping sensitivity functions (i.e., four functions: quadratic and sigmoidand their inverse variants) with arm (Adist and Aori) and wrist movements(Wangle) leads to 4×4×4 = 64 combinations. However, investigating all thesecombinations with a study is practically unfeasible. Therefore, I decided onthe combinations based on a pilot study and prior work related to mappingsensitivity functions to motor controls. In the pilot study (N=4), I only494.2. STUDY 4: EXPLORING SENSITIVITY FUNCTIONSFigure 4.6: (a-c) The result of the sensitivity curve combinations is shownwith gradients. A stronger color in the gradient indicates higher stability(i.e., coarse movements), and a weaker color indicates lower stability (i.e.,fine-grained movements). Note that the effect for arm movement (Adist andAori) is only shown in (a) SSQ and (b) IIQ, excluding the wrist movement(Wangle). (c) Wrist uses a quadratic function where upward/extension anddownward/flexion extreme angles benefit from a more stable control thanthe middle range.used four sensitivity functions (i.e., quadratic and sigmoid and their inversevariants) to map with arm (Adist and Aori) and wrist movements (Wangle).I used the same teleportation tasks as in Study 1 where participants wererequired to teleport to a platform. However, I only investigated difficultconditions: 10.5 m distance, and height variations from 0 m to 5 m and from5 m to 0 m conditions. During the pilot study, I observed that quadraticfunctions works better for wrist movement mapping, and sigmoid or inversesigmoid functions are more suitable than others for arm movement mapping.Interestingly, I observed that participants’ performance degraded when usingtwo different functions for Adist and Aori. Therefore, I came up with thefollowing techniques consisting of PAW with added sensitivity functions (i.e.PAWS+S where S = SSQ, IIQ, SIQ, ISQ). Names are given only based onthe sensitivity function initials for the sake of brevity. Note that for all thetechniques except for PAW, I used a quadratic function to map wrist angles504.2. STUDY 4: EXPLORING SENSITIVITY FUNCTIONS(Wangle) to the pointer movements.− PAW: PAW involves linear functions to remap all movements - thearm horizontal extension (Adist), the arm vertical movement (Aori),and the wrist angles (Wangle) to control the parabolic pointer move-ment.− SSQ (Sigmoid-Sigmoid-Quadratic): SSQ uses the same Sigmoid func-tion for both the horizontal arm extension (Adist) and the vertical armmovement (Aori). I anticipate that SSQ can provide less sensitivity(high degree of stabilization and control of the pointer) when the armis high and/or extended, and more sensitivity when the arm is low ornear the user’s body (Figure 4.5a, and 4.6a).− IIQ (Inverse Sigmoid-Inverse Sigmoid-Quadratic): IIQ uses an inversesigmoid function for both the horizontal arm extension (Adist) and thevertical arm movement (Aori). This technique would allow users torapidly move the pointer with more sensitivity when the arm is highand far, and the inverse when the arm is near or low (Figure 4.5a, and4.6b).− SIQ (Sigmoid-Inverse Sigmoid-Quadratic): SIQ uses a sigmoid func-tion for the horizontal arm extension (Adist) and an inverse sigmoidfunction for the vertical arm movement (Aori). With this technique,users have a less sensitive pointer (i.e., more stabilization) when thearm is low and far, and the inverse when it is high and close (Figure4.5a).− ISQ (Inverse Sigmoid-Sigmoid-Quadratic): This mapping - the oppo-site of SIQ - uses an inverse sigmoid function for the horizontal armextension (Adist), and a sigmoid function for the vertical arm move-ment (Aori). ISQ would offer users less sensitive pointers (i.e., morestabilization) when the arm is high and close, and the inverse whenthe arm is low and far (Figure 4.5a).4.2.4 ParticipantsFifteen right-handed participants (mean age = 25.1, SD = 4.04, meanheight = 163.7, SD = 6.18, 5 females), between the age 19 and 32 wererecruited from a local university using on-campus flyers and word-of-mouth.Participants were compensated with a $15 gift card for their participation.514.2. STUDY 4: EXPLORING SENSITIVITY FUNCTIONSEleven participants had prior experience using VR applications. None ofthem participated in the first study.4.2.5 Experimental Tasks, Procedure, and DesignTaskI use the same teleportation tasks described in Study 1 where partici-pants were required to teleport to a platform marked with a semi-transparentsphere. However, I made some changes in Distance and Height variation tofocus on difficult situations. Study 1 revealed that participants were signif-icantly slower when the target platforms were located far and when theywere required to teleport to high upward or downward trajectories. I hencekeep only distant targets (7.5 m and 11.5 m), and 3 height variations (from0 m to 0 m, upward from 0 m to 5 m, and downward from 5 m to 0 m).ProcedureThe study starts with a similar briefing and settings to study 1. First,30 practice trials are given so that participants could familiarize themselveswith VR teleportation without any personalization technique (baseline con-dition for study 1). After the 30 generic trials, participants started theexperiment using each evaluated technique. They completed 15 practice tri-als before starting each new technique, with a 2-minute break in-betweento avoid arm fatigue. Each participant completed 2 Distance × 3 Heightvariation × 2 blocks × 5 repetitions for each technique = 60 trials for eachTechnique. Participants had a 1-minute break between the two blocks of tri-als, and another 2-minute break in-between techniques. Once participantscompleted all trials, they filled out a questionnaire to provide feedback oneach Technique. A session lasted approximately 60 minutes including shortbreaks and practice trials.Study DesignThe study used a 5×2×3 within-subjects design for factors Technique(PAW, SSQ, IIQ, SIQ, ISQ), Distance (7.5 m and 11.5 m), and Heightvariation (from 0 m to 0 m, upward from 0 m to 5 m, and downward from5 m to 0 m). With 2 blocks of 5 repetitions, each participant performed atotal of 300 trials. The order of Technique was counter-balanced betweenparticipants and Distance and Height variation were presented in a randomorder for each technique. I logged Trial Time and Error Rate where the524.2. STUDY 4: EXPLORING SENSITIVITY FUNCTIONSTrial Time is measured by the time a user took to teleport from the currentposition to the target location. Error Rate is defined by the number of timesthe user attempted to teleport to a non-target destination. I also collectedsubjective feedback on techniques via questionnaires.4.2.6 ResultsTrial timeI analyze log-transformed trial time with repeated measures ANOVAand post-hoc pairwise comparisons with Bonferroni corrections. Techniquehas significant effects on trial time (F4,41 = 4.11, p < 0.01, η2 = 0.23).The mean trial times are 2.96s ([2.84, 3.01]) for PAW, 2.71s ([2.61, 2.81])for SSQ, 2.72s ([2.61, 2.83]) for IIQ, 2.97s ([2.83, 3.11]) for SIQ, and 2.88s([2.76, 2.99]) for ISQ (Figure 4.7). Post-hoc pairwise comparisons revealthat SSQ and IIQ are significantly faster than PAW (both p < 0.001), SIQ(both p < 0.01), and ISQ (both p < 0.01). No other statistically significantdifferences between the techniques were observed.Figure 4.7: Mean trial time across the techniques for different height varia-tions. Error bars show the 95% CIs.Results show that Height variation (F2,41 = 59.00, p < 0.001, η2 = 0.81)and Distance (F1,41 = 87.59, p < 0.001, η2 = 0.86) have also a significanteffect on trial time. Post-hoc pairwise comparisons show differences betweenall height variation pairs and between the two distances: Participants arefaster when teleporting to close targets (mean = 2.66s [2.59, 2.74]) or with-out height difference (mean = 2.63s [2.54, 2.71]) than when teleporting to534.2. STUDY 4: EXPLORING SENSITIVITY FUNCTIONSdistant (mean = 3.03s [2.96, 3.10]) or elevated locations (mean = 3.11s [3.02,3.20]). While there is no significant interaction effect between Technique andDistance (F4,41 = 2.40, p = 0.06), results show interesting interaction effectsbetween Technique and Height variation (F8,41 = 2.25, p < 0.05, η2 = 0.14)- although with a small effect size. There are significant differences betweenevery Height variation for every technique (all p < 0.05) except for SSQ andIIQ. Indeed, with IIQ, trial time is resistant to height variation for the twofirst levels (mean = 2.45s [2.28, 2.62] and 2.63s [2.45, 2.83]) (p > 0.05), anddifferent only with the largest height variation (mean = 3.06s [2.92, 3.21])compared to the two smaller variations (p < 0.001). SSQ shows anothertrend: the smallest height variation (mean = 2.58s [2.34, 2.77]) leads tofaster trial times than with larger variations (mean = 2.72s [2.68, 2.98] and2.83s [2.57, 2.88]) (p > 0.05), but there is no effect on trial time between thetwo larger variations (p > 0.5). Thus, with none or small height variations,only IIQ leads to significantly faster trial times than PAW (p < 0.05), whilewith large height variations, only SSQ is significantly faster than PAW, SIQ,and ISQ (all p < 0.05).Error RateI analyze the error rate with Friedman tests with Wilcoxon tests forpost-hoc pairwise comparisons. The overall error rate is 16.1%. There is nosignificant difference in error rates regarding Technique (χ2(4, N = 15) =5.92, p = 0.21), Height variation (χ2(2, N = 15) = 1.73, p = 0.42), andDistance (z = −1.48, p = .14). Participants perform more errors whenexecuting far (10.5m, mean = 18%, SD 0.70) and elevated (5m, mean =16%, SD 0.72) teleportation (Figure 4.8).Preference Scores and Subjective FeedbackSimilar to study 1, I collected users feedback on their preference, fa-tigue, speed, accuracy and easiness on a 5-point Likert scale for each tech-nique. I analyze the data with Wilcoxon Signed Ranked Tests. I observethat Technique has no significant effect on users’ preference (χ2(4, N =15) = 3.51, p = 0.48), fatigue (χ2(4, N = 15) = 5.83, p = 0.21), speed(χ2(4, N = 15) = 5.64, p = 0.23), accuracy (χ2(4, N = 15) = 5.13, p = 0.27),and easiness (χ2(2, N = 15) = 10.78, p = 0.04). There is no other pairwisestatistically significant differences.Feedback from participants reveals that two preferred IIQ than other544.2. STUDY 4: EXPLORING SENSITIVITY FUNCTIONSFigure 4.8: Error rate across the techniques for different distances. Errorbars show the 95% CIs.techniques. One participant mentioned, “[I] especially liked IIQ as it feltlike I could move the pointer without trying to hold my hand steady” [P3].Another participant commented that “IIQ helped me with my shaky hands,and It was hard with other techniques, especially with the target located at ahigh elevation” [P5]. Participants also identified SIQ as a difficult techniquefor teleportation. One participant expressed that “SIQ was a very hardtechnique for high targets” [P4]. They also noticed that some techniqueshelped them reach close targets. One participant commented “For the ISQand baseline techniques, my accuracy was high when the targets were locatedat a closer distance” [P6].4.2.7 DiscussionI observed that using sensitivity functions to counteract the reductionof stability while offering more control for teleporting to distant and ele-vated targets results in faster performance for SSQ and IIQ. My hypothesisthat using an asymmetric sensitivity function that provided more coarsecontrol for such targets would result in better performance for high tar-gets was also proven correct as SSQ provided the best performance. Assuch, the sensitivity functions were demonstrated to be a crucial additionto the full personalization option. PAWS (PAW + Sensitivity Functions)554.2. STUDY 4: EXPLORING SENSITIVITY FUNCTIONSprovides an improved bare-handed teleportation experience. However, IIQ,the mirror opposite of SSQ, also improved performance users’ teleportationperformance. I observed that this is primarily due to the lower Height vari-ation (e.g., from 0 m to 0 m) and near Distance (e.g., 7.5 m) used in thestudy.Interestingly, I also observed that SIQ and ISQ didnt perform better thanPAW, and worse than SSQ and IIQ. Note that SIQ and ISQ used oppositesensitivity functions for arm movements, i.e., the horizontal arm extension(Adist) and the vertical arm movement (Aori). This suggests avoiding usingdissimilar sensitivity functions for closely coupled motor movements.56Chapter 5Design Guidelines,Limitations and Future WorkI present my findings as design guidelines for hands-only controller-freelocomotion and teleportation based on the results of my studies. Theseinsights are gathered from four studies on (i) exploring gestures for telepor-tation sub-activities, (ii) evaluating user performance on several gesture setsand two hands-only teleportation techniques (WriArm, Arm), (iii) exploringthe effect of different levels of personalization on users physical attributes,and (iv) investigating sensitivity functions to motor movements for improvedpersonalization of teleportation.5.1 Design Guidelines5.1.1 Gestures for Teleportaion− Gesture Sets: Instead of a single gesture, a set of gestures should beused for complex interactions such as teleportation, where users haveto perform different sub-activities using separate but cohesive gestures.My study results showed that these gestures could affect the overallteleportation task performance. Therefore, users should have a quickand easy option to switch between their preferred gesture sets, as dif-ferent gesture sets might be suitable or preferred by different users.In addition, I suggest designers consider explicit gestures rather thanusing timeout-based selection (e.g., dwell [57, 62, 83]) to allow usersmore control over the confirmation. Leveraging gestures to explicitlyand immediately confirm teleportation gives more control to the user,reduces the time needed to move and better represents real-world us-age.− Simpler is Better: my design study results showed that some ges-ture sets were rated higher than others. Therefore, designers shouldconsider why certain gestures might be preferred by participants for575.1. DESIGN GUIDELINESparticular type of activities. For instance, when an action is to be re-peated frequently, simpler gestures with small movements are better.I found that the gesture sets that used simple and small movementswere preferred by designers for frequent tasks (e.g., confirming tele-portation). I also observed that users performed better with thesegestures and experienced less fatigue. Thus, when possible, repeatedgestures should be kept simple and more complex gestures should beused only for actions that are not frequently needed.− Elicitation: I suggest using domain experts to find suitable gesturesets. My experimental results showed that the top two gesture sets(i.e., gesture set 3 and 4) preferred by the designers also received higherratings by users for speed, accuracy, ease and lower ratings for fatigue.As the design space for gestures is very large, considering all possiblegestures in a study is very complicated and time-consuming. Whereasdesigners are able to narrow down the design space to a smaller num-ber of gestures for a more streamlined study design. Therefore, I rec-ommend designers consider recruiting domain experts as participantswhile running any design study.5.1.2 Hand-Based Teleportation Pointing− Techniques: Designers should consider providing users with more flex-ible control over the teleportation pointer. Using the flexibility ofthe wrist’s movement to provide additional control over the parabolicpointer allowed faster, easier and more comfortable navigation to high,low and distant destinations. This establishes that even though arminput is suitable for coarse movements, incorporating wrist movementwould allow fine, faster and easier control of the parabolic pointer,especially for practical environments with varying elevations and dis-tances.− Resting Arm: In my studies, after 10-15 minutes, I observed usersresting their elbows on their bodies to gain stability and reduce fa-tigue from holding their arms in mid-air. When designing hands-onlytechniques for usage over long-periods of time, this factor should beconsidered. Users should be able to perform a full range of tasks whiletheir arm is supported by their body and should not be required tohold their arm in mid-air for long periods of time.− Gestures and Hand Pointing: When the hand is being used for both585.1. DESIGN GUIDELINESteleportation pointing and performing gestures, the act of performingthe gesture can have some effect on the chosen pointer location. Thelast moment movement during the gesture can move the destinationwhich can result in slight errors and the user not reaching their exactchosen destination. Using the last stable location before the gestureis performed can be a possible solution to this problem. I suggestthat last-moment changes in pointer location should be consideredand compensated for when designing hand-based pointing techniquesfor teleportation techniques.5.1.3 Task Design− Variety: Realistic tasks are more suitable for evaluating locomotiontechniques. My results showed that using realistic task designs can beused to evaluate the realistic performance of locomotion techniques.These task designs consider a combination of factors such as elevationchanges, distances, and the angle of travel. Additionally, I found thatwhen using small hovering platforms, some users’ fear of falling wastriggered because they could see too far over the edge. Using largerplatforms largely solved this issue.− Difficulty: Teleportation tasks should have both easy and difficult des-tinations to simulate real-world scenarios and provide more diverseuser data. Difficult destinations force users to use the maximum andminimums of their movement range and flexibility, providing insightsinto their performance in those conditions.5.1.4 Physical Attributes And Personalization− Consider Full Personalization (from study 3): In controller-basedgames (e.g., joystick-based video games), users commonly controlvirtual characters offering the same abilities to every user regard-less of users’ physical attributes. However, in virtual reality, userscommonly are the characters in the virtual environment, and theirphysical attributes such as height and arm length can result in differentexperiences due to differences. I hence advise a fully personalized (i.e.,PAWS) teleportation technique for significantly faster performancethan partial (e.g., PAdist in this case) and no personalization (i.e.,Baseline in this case). In addition, users’ performance and subjectivefeedback also reflect that a fully personalized hands-only teleportation595.1. DESIGN GUIDELINEStechnique helps users with reduced arm movements and wrist flex-ion/extension abilities. Therefore, I suggest that future researchersand developers to consider providing fully personalized VR applica-tions, games, and interactions to enable better and equal experiencesto users regardless of their physical attributes.− Use Sensitivity Functions For Personalized Hands-Only Tele-portation (from study 4): The results of study 3 showed that users’performance degrades with personalized techniques when the targetsare located in a distant and highly elevated location. Consequently,I investigated sensitivity functions for mapping hand movements tothe personalized teleportation pointer and found that sensitivity func-tions can provide crucial performance benefits by remedying the lossof precision caused by full personalization. I suggest designers con-sider sensitivity functions to offer users control over the teleportationpointer.– Map Motor Profiles to Sensitivity Functions: I suggestmapping asymmetric motor movements with asymmetric func-tions and symmetric motor movements with symmetric functions,also suggested in [46, 74]. Matching the symmetry and asymme-try of motor movements to sensitivity functions can allow thesensitivity functions to counteract performance loss in the motormovement postures by matching the sensitivity accordingly.– Combine Similar Sensitivity Functions For ConnectedMovements: Using similar sensitivity functions for coupled move-ments (e.g., vertical and horizontal arm movements) provides bet-ter performance than using dissimilar functions. This is intuitiveas using opposing sensitivity functions for coupled movementswould result in unnatural and unpredictable sensitivity to theuser due to the mismatch in sensitivity areas of the functions. Isuggest designers avoid using dissimilar sensitivity function com-binations for closely coupled motor movements as it decreasesperformance.– Choose Suitable Sensitivity Function Combinations: I ob-served in the context of my task design that using SSQ helpedreduce the trial time for high and distant targets, whereas IIQ re-duced the trial time for low and close targets. This indicates thatcertain combinations of functions provide better performance forcertain types of navigation tasks. For example in the part of a605.2. LIMITATIONS AND FUTURE WORKVR experience where the terrain is relatively flat and objectivesare close by such as a virtual village or office, IIQ can be used forbetter results. On the other hand in a VR experience where theuser is traversing a mountainous or vast area, SSQ can be used toprovide better performance to users. As such, designers shouldalso consider context-based sensitivity functions for teleportationtasks in VR environments.5.2 Limitations and Future WorkIn this section, I will discuss some inherent limitations of my thesis anddiscuss possible future extensions.5.2.1 Technique− Pointer Type: My techniques focused solely on the parabolicpointer as it is used widely in teleportation and is more intuitive forusers [35]. However, recent studies have explored teleportation tech-niques that use different pointer types (e.g., Linear pointer [62]) andprovide additional features (e.g., allow users to select their orientationafter teleportation [35]). Further studies are needed to evaluate theperformance of my improved wrist-based technique on different typesof pointers mentioned above.− Sensitivity Combinations: I only explored a limited combinationof sensitivity curves by reviewing previous works and running pilots,thus excluding an exhaustive investigation of all possible combinations.Future studies can be conducted by incorporating more combinations.My task design did not consider any occlusions or obstacles that mighttypically be present in realistic navigation tasks in 3D environments.− Privacy: Hand-based teleportation such as WriArm and PAWS mayraise privacy concerns in public places as hand tracking is done via thedevice’s front-facing cameras, which also capture users’ surroundings.For example, the device might record other people sharing the pub-lic space, thus violating their privacy without their consent. Futurestudies need to investigate users and passer-by’s privacy concerns.615.2. LIMITATIONS AND FUTURE WORK− Error Rate: Even though PAWS provides faster performance thanstandard techniques, users were not able to reduce the relatively higherror rate (around 15%) for hands-only teleportation. I attribute thishigh error rate to my difficult conditions (i.e., high and distant targetspushing the boundaries of the reachable area), but this is still a notablelimitation.5.2.2 Study− Participant Diversity: I acknowledge the disproportionate ratioof male to female participants in my design session and also in myuser studies. Additionally, all of the participants are from the North-American region. For future studies, feedback from participants ofother genders and from varying regions may be incorporated, whichwill improve the diversity of the participants.− Participant Numbers: My design study exploring dedicated andunique gestures to enter or exit the teleportation system had a limitednumber of designers (N=8), an elicitation study with more participantscould be explored in future work. My user studies had a limited num-ber of participants (N = 12, 12, and 15 respectively for Study 2,3,4).This is a limitation, however, similar sample sizes (N=12) are one ofthe most commonly reported sample sizes for HCI research [13]. Dueto the number of experienced VR participants being disproportionatein my studies, a comparative analysis between new VR users and ex-perienced VR users was not done. This can be explored specificallyin future work. Nevertheless, ours is an initial exploration of hands-only teleportation for VR, future studies can include a large numberof participants to further ascertain my findings.− Fatigue: In my studies, I used post-study questionnaires to gathersubjective feedback from participants about arm fatigue on both ofthe techniques; however, a quantitative measure of arm fatigue [43]has been shown in previous works that explored hand movements.A quantitative measure of arm fatigue can be considered for futurestudies.− Setting: I asked participants to stand in a location during the studieswhile completing the teleportation tasks. Although interaction in thestanding posture provides users with more immersive and engagingexperiences, the sitting posture offers the user higher levels of safety,625.2. LIMITATIONS AND FUTURE WORKcomfort and accessibility [95]. Future work could consider both stand-ing and sitting conditions for evaluating teleportation techniques.− Multiple Teleportation: I asked users to only navigate to locationswhen there is a platform and did not include multiple teleportationsin my studies. However, I included single teleportation for differentdistances (i.e., near, mid, and far) and heights (flat, high, very high) torecord users’ performance in the combination of different distances andheights. Multiple teleports are also not feasible in some existing VRgames such as AstroBot VR, Lucky’s Tale where users jump from plat-form to platform. Additionally, [11, 35] showed that repeated telepor-tation causes disorientation and may result in increased disorientation,thus I only focused on single teleportation. Multiple teleportations inopen environments can be explored as possible future work.− Controllers: My exploration of hands-only teleportation presentssome limitations. For example, I focused primarily on controller-freeteleportation and did not compare users’ performance with physicalcontrollers. As controller-free hand tracking based techniques are stillin their infancy, previous work in hands-only methods [11, 83] havealso focused only on hand-based teleportation - not performing a di-rect comparison to controllers. As in this thesis, I decided to imple-ment the techniques without any special hardware such as motion-trackers. Therefore, a comparison with controllers would also be un-fair as controllers provide Infrared and IMU-based tracking resultingin very stable and precise tracking data when compared to in-headsetcamera-based hand tracking. Comparison for personalized techniqueswould also be unpredictable as the motion ranges are different on acontroller and to our knowledge, no previous work exists that exploredpersonalizing controller movements for teleportation. A future studycan explore these limitations of my thesis, and also explore personal-ization for controller-based teleportation as the parabola launch anglein controllers depends on the radial and ulnar deviation of the handthat holds the controller.− Fitts Law: Hand-based straight-line aiming for 3D Fitts law hasbeen explored [44, 53]. Therefore, rather than focusing on Fitts law,I investigated personalization in the context of teleportation and keptmy task design similar to previous work [19, 35, 62]. However, a Fitts’law study could be conducted in the future to examine predictionmodels for rapid pointing for teleportation, allowing insights into the635.2. LIMITATIONS AND FUTURE WORKtrade-off between speed and accuracy for hand-based, controller-freeselection in VR.To my knowledge, this research is among the first to explore personalizedhands-only teleportation using the wrist and the arm. The techniques arealso implemented on commercially available VR headsets with no additionaltrackers. However, further research can explore many of these limitationsand investigate personalized hands-only teleportation and locomotion.64Chapter 6ConclusionModern commercially available virtual reality headsets now come equippedwith on-headset cameras that provide hand-tracking capabilities. This opensup vast possibilities for controller-free virtual reality interactions. Withhands-only interactions, users no longer have to hold a controller and pressbuttons to perform actions. When the controller is removed, hand interac-tions such as grabbing, picking up objects, manipulating objects, poking,pushing, holding, and more can be done naturally using the hands them-selves [66], similar to the real world. Researchers have shown that thesehands-only interactions are intuitive, easy to use, and have lower task loadswhile still providing a natural and immersive experience [48, 50, 81, 86].However, removing the controller also means losing access to commonly usedcontroller-based locomotion techniques, such as joystick movement and tele-portation. These locomotion techniques are crucial in VR as they allowusers to navigate large virtual environments without requiring a large phys-ical area. Without the availability of a hands-only locomotion technique,users would need to pick up a controller, use controller-based locomotion,and then put the controller down again to return to hand interactions. Sinceteleportation is one of the most widely used locomotion techniques in com-mercial applications and in previous work [6, 18, 33], a fast and usablehands-only teleportation technique could be an effective solution to theseproblems.In my thesis, I design and explore controller-free hands-only teleportationin VR. Through a series of design and user studies, I develop a novel per-sonalized teleportation technique that incorporates users physical attributes,allowing them to perform teleportations in VR without using any controllers.First, I design and evaluate WriArm, a novel wrist and arm-based tele-portation technique for VR. Previous works have mostly focused on dwell(timeout) [11, 62, 83] based confirmations for teleportations, did not providealternatives to all controller-based teleportation sub-activities, and did notaccount for interference with other natural hand gestures. With a designstudy, I explore suitable gesture sets for hands-only teleportation, providinggestures for each possible teleportation sub-activity, including activation,65pointing, confirmation, and cancellation.I further evaluate the gesture sets in combination with my novel wrist andarm-based technique WriArm through a user study. My results show usersperform faster on gesture sets with natural hand-poses and smaller handmovements (Gesture set 4) and also prefer them for frequently performedactions. Results also show that using both arm and wrist movements (i.e.,WriArm) results in faster teleportation compared to the arm-based tech-nique. In addition, participants found WriArm to be easier and preferred itmore than arm-based teleportation. As hand-tracking becomes a commonfeature of commercially available headsets and grows more prominent in VRapplications, WriArm can provide a fast and usable hands-only teleportationtechnique for controller-free virtual reality.WriArm has a dependency on users’ physical attributes, resulting indifferent experiences for users with varying attributes. Unlike controllers,which provide a uniform experience to users with different attributes, hand-only interactions depend on attributes such as height, arm length, and wristflexibility. While it offers a general experience, it doesn’t account for thesedifferences. To address this issue, I investigate personalized teleportationtechniques that consider users’ attributes, such as arm movement and wristflexibility ranges for hand-based teleportation techniques.I conduct a study exploring wrist and arm based teleportation techniqueswhile varying the degree of personalization (i.e., one-attribute personaliza-tion, two-attribute personalization, and all-attribute personalization). Theresults show that a higher degree of personalization improves user perfor-mance with teleportation. However, the performance degrades for high anddistant targets due to reduced motor stability and control. I conduct a fur-ther study to investigate these motor range boundary issues. Specifically, Iexplore different sensitivity functions that provide increased or reduced con-trol and stability over users’ movements in the beginning, middle, and end ofthe motor movement range for hands-only personalized teleportation. Theresults show that asymmetric functions for arm movements and symmet-ric functions for wrist extension/flexion improve users’ overall performance,including navigation of high and distant targets. These improvements en-hance the personalized technique to users’ motor abilities. Thus, I introducePersonalized Arm and Wrist Movements with Sensitivity functions (PAWS),which provide users with an enhanced controller-free teleportation experi-ence.In conclusion, wrist and arm-based hands-only teleportation is an ef-fective and fast technique for navigating VR environments, but its efficacyis dependent on users’ physical attributes. By fully personalizing wrist and66arm movement-based teleportation, users can enjoy an improved, faster, andcontroller-free hands-only teleportation solution for navigating their environ-ments. PAWS is a promising technique for future bare-handed VR teleporta-tion designs, as it allows users to move around their virtual worlds efficientlywithout requiring controllers or any special hardware devices. With PAWS,users of different physical attributes can freely explore large virtual worlds,even in limited physical spaces. Additionally, by removing the need for con-trollers, users no longer have to pick up and put down controllers in themiddle of hand-based interactions, opening up a vast design space for notonly teleportation but also overall controller-free hand-based natural VRinteractions.67Bibliography[1] Arazy, O., Nov, O., and Kumar, N. Personalityzation: Ui per-sonalization, theoretical grounding in hci and design research. AISTransactions on Human-Computer Interaction 7, 2 (2015), 43–69. →pages 5, 18[2] Azmandian, M., Grechkin, T., Bolas, M. T., and Suma, E. A.Physical space requirements for redirected walking: How size and shapeaffect performance. 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Thesis/Dissertation
eng
Computer Science
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Hasan, DMohammad Khalad
Exploring personalized hands-only teleportation in virtual reality
Text