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The design and implementation of a scheduler and route planner for wheelchair users Yang, Suling 2006

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The Design and Implementation of a Scheduler and Route Planner for Wheelchair Users by Suling Yang B.S., Simon Fraser University, 2004 A THESIS S U B M I T T E D IN PARTIAL F U L F I L L M E N T OF T H E R E Q U I R E M E N T S FOR T H E D E G R E E OF Master of Science in T H E F A C U L T Y OF G R A D U A T E STUDIES (Computer Science) The University of British Columbia December 2006 © Suling Yang, 2006 Abstract Currently, Global Positioning Systems and other route-finding systems work well for wheelchair users outdoors. Indoor information is usually not provided. However, it is more important for wheelchair users to know the indoor maps of routes accessible to them. Where the closest elevator is, which door is accessible to them, and how long it takes to get to a destination are major route-finding problems for wheelchair users. Nevertheless, there is no existing system that shows up-to-date and detailed information on route accessibility. A system that works for the independent user would provide more accurate help for wheelchair users. For example, a strong young man in a wheelchair will have less difficulty getting through moderate ramps and rough roads than the el-derly who tend to prefer smooth routes. Therefore, we are motivated to create a system that works according to the client's ability, so that it can figure out which path is best for the client under certain constraints. There are several existing systems that allow the user to easily control their wheelchairs. Some of these sys-tems use automatic or semi-automatic robotic wheelchairs, but they are limited to very small local area movement. Such systems focus on hardware components of an auto-wheelchair. Our system, which provides help in pathfinding, can cooperate with these hardware-equipped wheelchairs. The success of related work on daily activity reminder systems has motivated us to build a simple scheduler that works in conjunction with the route planner. As well as designing and creating the new system, we have analyzed and implemented a new efficient pathfinding algorithm, which is the major contribution of this thesis. The algorithm deploys the hierarchical structure of real-life maps. Assuming that the distance within an abstract high-level node is significantly shorter than along high-level edges, the algorithm can prune away irrelevant paths. Runtime analysis and experimental results show that this algorithm is efficient in numerous scenarios. n Contents Abstract ii Contents iii List of Tables vi List of Figures vii Acknowledgments • x 1 Introduction 1 1.1 Motivation 1 1.2 Challenges and Contributions 2 1.3 Thesis Outline 5 1.4 A Note on Technology 6 2 Related Work 7 2.1 Reminder Systems ? 2.1.1 Microsoft Outlook 8 2.1.2 Daily Activity Planners for Special Populations 8 2.2 Route Helper Systems 9 2.2.1 A Monitoring System to Provide Pathfinding Help 9 2.2.2 Activity Compass for People with Alzheimer's Disease . . . . 9 m 2.3 Robotic System for Wheelchair Users 10 3 The System Design 12 3.1 Participants and Design Specifications 12 3.2 Paper Prototype (Low-Fidelity Prototype) 13 3.3 Medium-Fidelity Prototype 15 3.3.1 The Main Screen 15 3.3.2 Scheduler Interface Design 16 3.3.3 Display Paths 18 3.4 High-Fidelity Prototype 20 4 Funct ional i ty 32 4.1 Scheduler and Reminder 32 4.1.1 Basic Scheduler Functionality 32 4.1.2 Reminder Function for Individual Clients 33 4.1.3 Cooperation between Scheduler and Route Planner 35 4.2 Hierarchical Shortest Pathfinding 36 4.2.1 Previous Work . 37 4.2.2 The Algorithm 38 4.2.3 A Hybrid of HSP and A * (HSPA*) 42 4.2.4 Analysis of Running Time . . 42 4.2.5 Comparison and Results 44 4.2.6 Route Planner for People with Disabilities 46 4.3 Alternative Path for Individual Clients 47 4.3.1 Reject the Current Location 47 4.3.2 Recalculate a New Path 48 4.4 Database Management 49 4.4.1 Schedule Importing and Exporting 49 4.4.2 Map Representation 50 iv 5 Implementation 62 5.1 Inheritance and Polymorphism in Java 62 5.2 System States and Activities 64 5.2.1 Computing the Shortest Path 66 5.2.2 Prompting Reminder Messages 66 6 Experiments and User Study 68 6.1 Motivating Scenarios 68 6.2 Evaluators and Task Guidelines . . 69 6.3 Evaluation Results and Recommendation 71 7 Conclusions and Future Work 73 7.1 Conclusion 73 7.2 Discussion and Future Work 74 7.2.1 Optimizing the HSP and HSPA* Algorithms 75 7.2.2 Improving Interface Implementation 75 7.2.3 Improving Database Management 76 7.2.4 Applying Advanced Machine Learning Method 76 7.2.5 Redesigning for Different Classes of Users 76 7.2.6 Future Development in the Market 77 Bibliography 78 Appendix A Consent Form for All Evaluators 81 Appendix B Questionnaire for All Evaluators 83 B . l Questionnaire for the Evaluators Who Participated in the Design Phase 83 B.2 Questionnaire for the Evaluators Who Participated in the System Evaluation Phase • • 83 List of Tables 3.1 Summary of participant information 13 4.1 Running times: The running times (in milliseconds) of four algo-rithms using examples where every abstract node has four or more sub-nodes (6) 47 4.2 An example of an event item in X M L element form 49 4.3 A simplified example of the representation of a campus in X M L form 52 6.1 Summary of participant information 70 vi List of Figures 3.1 The paper prototype of the system's main screen 14 3.2 Evaluation of the low-fidelity system prototype 15 3.3 The medium-fidelity main screen prototype 16 3.4 The prototype of the frame for adding a new event 17 3.5 The prototype of an information message and a confirmation message 18 3.6 The prototype for displaying a high-level path 19 3.7 The prototype for displaying a low-level path 20 3.8 Evaluation of the medium-fidelity system prototype 21 3.9 A screen shot of the Route Planner when it starts 23 3.10 A screen shot of the frame for adding a new event 24 3.11 A screen shot of the frame for adding a new event, with information entered 24 3.12 A screen shot of an information message 25 3.13 A screen shot of a confirmation message 25 3.14 A screen shot of a confirmation message 25 3.15 A screen shot of the Route Planner when a high level path is shown 26 3.16 A screen shot of the Route Planner when the user wants to view the location information of a place 27 3.17 A screen shot of the Route Planner when a low level path is shown . 28 3.18 A screen shot of the second picture of the low-level path 29 3.19 A screen shot of the third picture of the low-level path 30 vii 3.20 A screen shot of the fourth picture of the low-level path . . . . . . . 31 3.21 Evaluation of the high-fidelity system prototype'' 31 4.1 The frame for editing an event's information 34 4.2 (a) A prompted message for manual reminder; (b) A prompted mes-sage for automatic reminder 34 4.3 The frame for inputting user's information 35 4.4 Examples: (a) and (b) are maps of campuses comprising three and four buildings, respectively, where each building contains a number of rooms 38 4.5 Running time plots: (a): the running times of three algorithms from level 1 to 5; (b): the running times of three algorithms from level 5 to 8 45 4.6 Running time plots: (a): the running times of three algorithms in log-scale; (b): the running times of four algorithms in log-scale . . . 46 4.7 A screen shot of the system when the client arrives at a certain position 53 4.8 The rejection message if the client clicks the "Reject Location" button 53 4.9 A screen shot for updating information for an event 54 4.10 A screen shot of the system when a new high-level path is found . . 55 4.11 A screen shot of the system when it displays a new low-level path . . 56 4.12 A screen shot of the second picture of the newly found low-level path 57 4.13 A screen shot of the third picture of the newly found low-level path . 58 4.14 A screen shot of the calendar of Microsoft Outlook with a frame for adding a new appointment 59 4.15 A screen shot of Microsoft Outlook calendar with some events entered 59 4.16 A sequence of screen shots when exporting event information from Microsoft Outlook calendar 60 4.17 A screen shot of the frame for modifying or viewing map information 61 4.18 A screen shot of the frame for viewing campus information 61 vm 5.1 First cut at the Scheduler and Route Planner System design 64 5.2 Final cut at the Scheduler and Route Planner System design . . . . 65 5.3 The activity diagram for the Hierarchical Shortest Pathfinding algo-rithm 66 5.4 The activity diagram for the manual and automatic reminder function 67 6.1 Evaluation of the Scheduler and Route Planner system 72 ix Acknowledgments I would like to thank my supervisor, Alan Mackworth. I am grateful to him for his valuable guidance and generous support. I would also like to thank other peo-ple who provide me with great help. A special thanks is to Le Chang who helped me throughout the process of this research. Jim Little, Chao Yan, Mingyue Tan, Wei-Lwun Lu and Shuan Wang suggested and discussed algorithm designs and pro-gramming methods with me. Zicong Mai, Suwen Yang, Jie Zhao, Wei L i , Xun Sun, Yizheng Cai, Chen Yang, Lin Zhong and Matthew Chan are always generous with help. I am grateful to all the participants involved in the user study. In particular, I would like to thank Dave Symington and Yasaman Mahboubi for their collaboration and suggestions. I want to thank my father and mother, Huiwen and Miaoqiong, who provide me with continuous support and love. My brother, sister-in-law and their son share and increase my happiness. SULING Y A N G The University of British Columbia December 2006 x Chapter 1 Introduct ion 1.1 Motivation The population of wheelchair users is very significant and increasing dramatically [2]. Therefore, finding accessible wheelchair routes is an important problem. In developed countries, most buildings and public transportation are accessible, making the lives of people in wheelchairs easier. For example, these conveniences include elevators to each level of a building, ramps in addition to stairs and buses with accessible services. However, the routes to the closest elevator in a new building, temporary road conditions and bus schedules may be unknown to wheelchair users. Therefore, we were motivated to create route-planning software that can be installed on a small device to give wheelchair users route accessibility information while they are travelling. With the rapid development of related technologies, a number of computer scientists have been carrying out research to devise technology that will assist or replace caregivers for the elderly or for people with disabilities. Currently, even small devices such as laptops and PDAs can provide good pathfinding help for clients. With these advancements in technology, the implementation of route-planning software is now possible. A scheduler program can synchronize with a route planner to provide more accurate commuting information for clients, such as the remaining time to the next 1 destination and the means of transportation. A scheduler is a useful tool to remind people of their daily activities. Therefore, our software contains a simple scheduler that can transfer a schedule from and to Microsoft Outlook. After obtaining the destination from the scheduler, the route planner establishes some possible paths to the destination and displays the best one to the client. A possible path means a route that has acceptable obstacles based on the client's ability and shows the travelling time required before the start of the next event in the schedule. The client can make changes to his/her schedule, and the route planner will immediately re-compute a path to the new destination. The first stage of our project is to implement an algorithm to hierarchically find paths and obtain multiple levels of detail. For example, to find a path between two rooms in two different buildings, we would find a path between the two buildings first, and then refine the path to the two rooms. Since an abstract high-level path can provide a general plan, the client can have an impression of future routing. Instead of being presented with a cumbersome and lengthy low-level path, people, especially the elderly, would prefer a cognitively visible path. In the next stage, the software accommodates our scheduler and real-world maps, including indoor and outdoor applications. At the moment, the software has been tested with the campus map of the University of British Columbia (UBC). Moreover, a shared online database could be constructed. Thus, multiple clients can exchange information or update obsolete maps in real time. For example, if a road is under construction, a client can upload this information after he/she observes the surroundings, and other clients can then download it immediately. Finally, an extension to the software could include voice communication for the optically impaired. 1.2 Challenges and Contributions Although several existing systems are available to help people with disabilities, none of them show up-to-date and detailed information on route accessibility. As a com-2 bination of previous endeavors devoted to implementing robust route-planning and daily activity reminder systems, this thesis addresses. the goal of building user-friendly interfaces of the route planner and scheduler system for the elderly and people with disabilities. The system should provide immediate transportation in-formation to the clients and compute the best routes effectively and efficiently. In the process of accomplishing our goal, we were met with several challenges, which we discuss in the following sections. Cooperat ion between the Scheduler and the Route Planner . The coop-eration between the scheduler and the route planner is especially important. The Aphasia [17] and Nursebot [4] projects have proven successful in reminding users about activities, while the Activity Compass [22] and the CLever [9] project help users find correct paths. However, the researchers who developed these systems did not observe that the scheduler and route planner together can provide more detailed and accurate information. First, the scheduler should synchronize with the real time clock, so it can provide correct reminders. Our scheduler is designed to check the time every minute and remind users of upcoming events. Second, know-ing the destination of the next event, the scheduler should communicate with the route planner and transfer the information together with the remaining time to it. Third, the route planner should compute the best path. If the user needs to head to a different destination because of a change to his/her schedule, the route planner should react immediately. On the other hand, the route planner could announce the minimum required transportation time to the scheduler, which could provide an early reminder to the user. D a t a Col lect ion and Database Management. People have become aware of the accessible route-finding problem for wheelchair users, yet routing information to many places is not available. We decided to test our system on the U B C campus first, since static outdoor accessibility routing maps are already provided, making 3 data collection simpler [6]. However, the provided information involves only outdoor data, and it is not specific to the necessary level of detail. Since indoor structures are more complicated, the required data is more difficult to collect and store. Our route planner showing both indoor and outdoor accessible routes for wheelchair users at U B C contains data of several buildings on campus that were collected by an administrator. Moreover, architectures are organized hierarchically. For example, each room is in a building, which in turn is in a city. Maps in our experiments are constructed in such a hierarchical way in X M L nested tag format. However, such an X M L file for a large map is difficult to create and maintain. Therefore, we built a Java class that reads a map in X M L form and creates lists of architectural elements, each of which has a list of neighbours and a list of the corresponding distances. Other Java classes in this project can access these lists and perform actions on each architectural element. In addition, another class was built to modify changes to the map. Hierarchical Pathfinding Algorithms. Pathfinding on large-sized maps is time-consuming. Classical search algorithms such as A * and IDA* with accurate heuris-tics may solve difficult problems in polynomial time [10, 13]. However, in real-world pathfinding examples where the search space increases dramatically, A * and IDA* may not be appropriate. Hierarchical pathfinding algorithms that provide abstract plans of future routing, such as HPA* and PRA*, have been explored by previous researchers based on classical ones [8, 28]. Although the two hierarchical algorithms show improvement in efficiency, they only obtain near optimal solutions. In this thesis, we introduce the Hierarchical Shortest Path algorithm (HSP) and a hybrid of the HSP and A * (HSPA*) algorithms, which find optimal solutions in logarith-mic time for numerous examples. Our empirical study shows that HSP and HSPA* are superior to the classical algorithms in realistic examples, and our experimental results illustrate the efficiency of the two algorithms. 4 Java G U I Programming After the prototyping is done on paper, we imple-mented the system in Java. The interface design must be user-friendly and easy-to-use, since the target users are the elderly and wheelchair users. We chose the Java Foundation Classes (JFC) Swing packages to help us get the necessary software, because not only do the JFC Swing components include all the required classes for our system, but the containment hierarchy of Swing components simplifies imple-mentation of the system interfaces. However, there are some difficulties with Java GUI programming. First, layout managers are preferred, even if exact pixel place-ment is also available, since the latter can lead to less portable code. However, there are only six types of layout managers, namely, BorderLayout, FlowLayout, Card-Layout, GridLayout, GridBagLayout and BoxLayout. Second, the event-handling mechanism is based on the AWT event-handling model. In other words, an event listener is registered on an object whose corresponding component generates such events. Therefore, knowledge acquisition of Java GUI programming is necessary. The current version of our system accomplishes the basic functions of the proto-types created during the system design period. Further interface development may be required. 1.3 Thes is O u t l i n e Following and complementing this introductory chapter, Chapter 2 presents overviews of related projects and indicates our motivation in relation to their usefulness. Chap-ter 3 shows the designs of several main functions of the system, and these functions are explained in Chapter 4. Detailed implementations are illustrated by U M L dia-grams in Chapter 5. In Chapter 6, experiments are run based on a given scenario and a user study is conducted to evaluate the system. Finally, conclusions and future work are presented in Chapter 7. 5 1.4 A Note on Technology We used a tablet PC for this project, because it is easy to carry and the screen is not too small for people with vision problems. We chose a Fujitsu LifeBook Tablet P C running on an Intel Pentium M , since it is highly recommended by other researchers in our department. With the help of a Garmin GPS 10 system, which is small, the route planner works more effectively. 6 Chapter 2 Related Work With the increased need for caregivers for patients and the elderly, a number of cognitive assistive technologies (CATs) have been developed or are being developed. The Assisted Cognition project [12], the Nursebot project [4], the Aphasia project [17], the CLever project [1] and the Aware Home project [3] are some interesting developments. These projects have shown great success in helping the elderly and people with mental problems. Systems used in these projects are generally cate-gorized into two types, reminder and routing helper systems. Also, a number of systems have been developed to improve wheelchair physical behaviours. Tin Man, Wheelesley and NavChair are robotic wheelchairs that can take commands from users and smooth the movement in wheelchairs. 2.1 Reminder Systems Our project is based on a reminder system, because with an accurate schedule, the destination (or sub-destination, e.g. a bus stop on the way to a doctor) can be easily obtained. Microsoft Outlook is a widely-used scheduler, while Autominder [16], ESI Planner II [20], and Adaptive Prompter [12] are reminder systems created for special populations. 7 2.1.1 M i c r o s o f t O u t l o o k There are numerous existing schedulers available on the web or with some enter-prise systems. These schedulers include WebCalendar, Google/Yahoo Calendar, Communications Express from SUN Java Enterprise System, Microsoft Calendar from Microsoft Outlook, etc. Microsoft Outlook is widely used, since it comes with the Windows systems. Moreover, the ease of use of Microsoft Calendar can comple-ment some simple and crude functions of our scheduler. One can easily record an appointment by clicking the "new" button, which leads to a window for inputting the appointment information and multi-attendee scheduling. One can also export some or all appointment information into a text file that can easily be read by other systems. Because of the above favorable aspects of Microsoft Outlook, we selected it as the augmentative scheduler for our system. 2.1.2 D a i l y A c t i v i t y P l a n n e r s for Spec ia l P o p u l a t i o n s The Autominder, a reminder system, of the Nursebot project is installed in a robot that provides reminders of daily activities to the clients [16]. The Autominder gets a list of tasks that the client needs to do, and provides an optimal schedule. A similar system, the ESI Planner II [20] from the Aphasia project, also provides functionalities such as a daily planner and a reminder system. The ESI Planner II is for people with aphasia, which influences one's communication ability, and is a software program installed on a PDA. The Adaptive Prompter [12] of the Assisted Cognition project and the P O M D P formulation of activities of daily living (ADLs) [7] provide reminders of the next step in the process of finishing an indoor task for people with Alzheimer's disease. The functionalities of estimating goals and providing reminders to forgetful people or people with mental problems have great potential to be broadly applied, since this population is rapidly increasing. However, these projects focus on populations with certain diseases, and none of them provides instant information about accessibility routing for people in wheelchairs when they 8 are travelling. 2.2 Route Helper Systems The main purpose of our project is to aid wheelchair users to find suitable routes. Several existing systems, such as Lifeline and Activity Compass, provide pathfinding help to clients when they are travelling. 2.2.1 A Moni tor ing System to Provide Pathfinding Help The Lifeline system from the CLever project is a tool for caregivers to monitor and support people with cognitive disabilities using wireless prompting devices, for example, cellular phones [9]. Users can complete tasks by following pre-planned actions. With this remote support, users can obtain a certain freedom and travel by themselves. More important, a single caregiver can monitor multiple travellers, which greatly reduces the workload and the need of caregivers. The caregiver plays the role of shared information retriever, so a self-learning and self-configuring system with a database shared with other clients may be able to take the place of a caregiver. In addition, the assumption that tasks are pre-designed can be eliminated if clients are allowed to update the shared information. Thus, instantaneous planning is possible by obtaining the latest information from the shared database. 2.2.2 Ac t iv i ty Compass for People wi th Alzheimer 's Disease The Assisted Cognition project [5] is of note here, in particular, the Activity Com-pass system. It learns from past daily transportation routines that have been per-formed by the user and estimates the destination of the current event. When an abnormal behavior occurs, for example, the user fails to get off the bus at the usual stop, the Activity Compass will alert the user [15]. However, the goal of the new event cannot be estimated by the Activity Compass until a partial event has been 9 completed, and it needs to learn from a large amount of past experience. One ex-tension of the Activity Compass, as stated in [12], is to incorporate information about the time of day and the day of the week into the learning model in order to enhance predictive power. This extension can be further expanded into a route planner with provided user activity schedules. With a known destination goal, the system can focus more on routing planning, such as the time needed to travel one path and the road conditions of a path that are suitable for the client. Moreover, the Activity Compass is created for people with Alzheimer's disease, which affects people's memory. Hence, a route planner based on a reminder system would provide greater help for this population of people. 2.3 Robotic System for Wheelchair Users The robotic wheelchair is designed mainly to help disabled people in navigation. At present, quite a few robotic wheelchair projects are underway, and some have already been applied to real life. In these projects, the navigation system and the simulator both play an essential role in the development of an intelligent wheelchair. Ojala et al. propose an approach to finding fundamental errors in the early design stage by using computer graphics animation and simulation [21]. They also refine the final product by letting the user take part in the design process. In [18], two low-cost wheelchair prototypes, TinMan I and II, are presented. These two prototype systems help the handicapped avoid obstacles, reach pre-determined destinations, and maneuver through doorways as well as narrow or crowded areas. Another au-tomated wheelchair, described in [29], makes use of a nonlinear signal processing circuit and pulse steering drive method to smoothly operate. Furthermore, func-tions for stopping at the desired destination and for preventing collision are imported into the design of the wheelchair navigation system. The paper by Yanco et al. in-troduces a robotic wheelchair named Wheelesley [30]. It includes indoor navigation and provides a user interface that can be easily adapted to a user's capabilities. In 10 another project, the NavChair assistive wheelchair navigation system is proposed to reduce the cognitive and physical requirements of managing a powered wheelchair [25]. The NavChair is an ordinary wheelchair system equipped with a DOS-based operating system, ultrasonic sensors, and an interaction module. Three operational modes are employed in this system, namely, general obstacle avoidance, door pas-sage and automatic wall following. The NavChair is further developed in [26] to embed a mechanism for automatically selecting the most proper operation mode by considering both the NavChair's current situation and its global location. The robotic wheelchair projects mentioned above have built strong wheelchair simulation or actual wheelchair systems with high-technical hardware. However, they are limited to use in a localized area. A route planner incorporated into such robotic wheelchair systems would work more intelligently for users. 11 Chapte r 3 The System Design We used prototyping, one of the Human Computer Interaction (HCI) related tech-niques, in the system design phase. Through the prototyping process, we can not only define the interfaces of our final product but also test attributes of it before it is complete. We employed paper mockup in the low-fidelity prototype period, because it is a cheap way of providing prototypes for testing and design improvements. In this chapter, we discuss the paper prototype in relation to the improvements and the medium-fidelity prototypes based on it. High-fidelity prototypes are shown and discussed in the last section of this chapter. 3.1 Participants and Design Specifications Our target users are wheelchair users, so we were encouraged to have wheelchair users as our participants. David, who has been using wheelchairs for more than thirty years, came to us from Disability Resource Center (DRC) at U B C and participated thoroughly in our project. Through discussions between David and the researcher, the problems of using a wheelchair and finding paths were identified. Finding the locations of elevators in an unfamiliar building is the core problem for people in wheelchairs. The time required to do so and the difficulty of accessing a path are secondary problems. Design specifications were also identified by David and the 12 researcher. The major design specifications include the following: • The route to the next destination should be shown clearly on the screen. • The route shown should be at an acceptable level of difficulty for the client. • A schedule following three to five events should be included. • Adding, deleting and modifying an event's information should be easily achieved from the main screen. Since people who do not use wheelchairs are considered as possible users of our system, we involved four non-handicapped people, Wei, Suwen, Arthur and Queenie, in our prototyping tests and system evaluation phase. Wei and Suwen are both computer scientists, so they use computers everyday. On the other hand, Arthur and Queenie rarely use computers. Although the frequent computer users outperformed both infrequent computer users and David, they all found the Sched-uler and Route Planner system useful for their daily activities. Table 3.1 summarizes the five participants' relevant information. The edu-cation field is the number of years of education completed by the participant. Table 3.1: Summary of participant information Participants Use Wheelchair Age Gender Education Computer Use Dave Yes 50 Male 18 Often Wei No 27 Male 18 Often Suwen No 26 Female 19 Often Arthur No 22 Male 16 Seldom Queenie No 25 Female 14 Seldom 3.2 Paper Prototype (Low-Fidelity Prototype) Once design specifications were defined, the author began by sketching the first draft of the main screen interface. The participants evaluated the sketch and giving 13 possible feedback and suggestions regarding improper designs. Gradually, the author collected all design ideas and optimized simplicity versus required functionality. With help from the participants, we finished the final paper prototype of the system's main screen, as shown in Figure 3.1. Although it is simple, it contains the two most important parts, the route planner and the scheduler. 1 Cfcv4 re--*3 \ A J O A , • 11-OOcLm: lunch |<P] ZOOIA-IM f 4 blO HAP E f i l l N/£U> Co-lencdtr- 1=11101 PKfcl/lOcXS ^; ooctm: &o -co S^BPI by lO-.oOam: feau (ei<-fs L2:00pm: LCI f o r ^ . J_:00f>m; OOyn b-ooym : Figure 3.1: The paper prototype of the system's main screen The participants were involved again in evaluating the final paper proto-type. Figure 3.2 shows the gradings for different aspects of the prototype. Interface satisfaction refers to the user's satisfaction regarding the locations of buttons, the number of clicks to access a certain function, etc. Functionality satisfaction indi-cates whether the user feels satisfied with the provided functions. Ease of use and willingness for future use are as stated. Three different color bars denote the grading from the three different participants. From this evaluation, the participants appear 14 interested in our system and are willing to use it in the future. Paper-Prototype Evaluation 100 -| 90 J Interface Functionality Ease of Use Willingness for Satisfaction Satisfaction Future Use Figure 3.2: Evaluation of the low-fidelity system prototype 3.3 Medium-Fidelity Prototype After design requirements were finalized and the paper prototypes evaluated, the researcher proceeded with medium-fidelity prototyping. Design adjustments were made based on final design requirements and implementation constraints. In this design phase, we used a vertical prototyping methodology that demonstrates the exact functionality for a small section of the entire product. Adding a new event and showing found paths are two main functions, and the interfaces for them are shown later in this section. Figure 3.8 shows the participant evaluation of this prototype. 3.3.1 T h e M a i n Screen Figure 3.3 shows the medium-fidelity main screen prototype. Note that we made several major changes based on the paper prototype. First, due to implementation difficulty, the "zooming in" and "zooming out" functions were eliminated. Instead, two levels of paths can be displayed, which will be discussed later. Second, a "Detail" 15 b u t t o n is a d d e d . B y c l i c k i n g this b u t t o n , t h e c l ient c a n v i e w the ent ire i n f o r m a t i o n of each event i n t h e space p r o v i d e d b e l o w t h e schedule, w i t h t h e d e t a i l e d p a t h s h o w n o n the r i g h t s ide of the screen. S o m e m i n o r changes i n c l u d e s s c r o l l bars a n d the date display . F i g u r e 3.3: T h e m e d i u m - f i d e l i t y m a i n screen p r o t o t y p e 3.3.2 Schedu ler Inter face D e s i g n A s speci f ied b y the p a r t i c i p a n t s a n d the researchers, users s h o u l d be able to create or delete event i n f o r m a t i o n easily. There fore , these f u n c t i o n s are accessible b y one c l i c k f r o m t h e m a i n screen. B y c l i c k i n g the " N e w " b u t t o n o n the l e f t - h a n d side of F i g u r e 3.3, a n e w w i n d o w is c r e a t e d , as s h o w n i n F i g u r e 3.4 (a). T h e c l ient t h e n f i l l i n t h e b l a n k s , as has b e e n d o n e i n F i g u r e 3.4 (b) . S u b j e c t , S t a r t D a t e a n d S t a r t T i m e fields are 16 required, while other fields are optional. However, Location is necessary if the client wants to know the path from the place specified in the From field or from the last destination. To simplify implementation work, the Start Time and Reminder fields are both required to be "hh:mm:ss A M / P M " form. For this example, if the client has a class at 12:30 P M , he/she should type in "12:30:00 P M " in the Start Time field. If he/she wants a reminder 10 minutes before the Start Time, the client should type in "12:20:00 P M " in the reminder field. After entering the information, the client clicks the "OK" button and an information message will be triggered, as shown in Figure 3.5 (a). The Start Time and Subject of the entered event are displayed in the event list. The client can click the "Detail" button to view the full information, which is shown in the space below the list. Add New Event • • 0 Subject: Start Date: (mm/dd/yyyy) Start Time: (hh:mm:ss AM/PM) From: Location: Reminder on/off: (hh:mm:ss AM/PM) OK Cancel Add New Event • CDS Subject: Go to classes Start Date: (mm/dd/yyyy) 11/25/2005 Start Time: (hh:mm:ss AM/PM) 12:30:00 PM From: ICICS10S Location: DEMP101 Reminder on/off: (hh:mm:ss AM/PM) 12:20:00 PM OK Cancel (a) (b) Figure 3.4: The prototype of the frame for adding a new event The editing event information function works in a similar way to adding a new event. Selecting an event and then clicking the "Edit" button triggers a window similar to the one in Figure 3.4 (b), where information is filled in. Editing one or more fields and then clicking the "OK" button updates the information for that 17 event. An information message follows to indicate that the change has taken place. To delete an event from the current schedule, the client just needs to select the desired event and press the "Delete" button. A confirmation message, shown in Figure 3.5 (b), is displayed to prevent accidental action. Inform atio riMe s s age: X , Q - \ Event "Go to classes" was PJ 3>J successfully added I OK C onfcr ra ati o n Me s s age X N Are you sure: you want to de lete. Eve nt "Gb to classes" ll YES NO M (b) Figure 3.5: The prototype of an information message and a confirmation message 3.3 .3 Display Paths . The main goal of the route planner is to display a suitable path for people in wheelchairs. The evaluation by the participants indicated that they appreciated that both an abstracted and a detailed paths are shown. First, the abstracted path can simplify the conception of the whole path for the client. Second, the detailed path shows the exact steps that the client needs to take. In the following sections, we discuss the medium-fidelity prototypes for displaying a path. Displaying High-Level Paths To display a high-level (detailed) path to the destination of an event, the client can select the event by clicking it from the event list. The event name is highlighted and a high-level path is shown on the right-hand pane if a path is found. This step is shown in Figure 3.6. The sub-destinations are shown as buttons, while the directions are shown as arrows. When the user clicks on one of the buttons, some information about the 18 corresponding (sub)destination is displayed in the text area on the left-hand pane. The current position of the client is denoted using a unique icon. The client can change his/her position by clicking twice on the button representing that place. Today is: 11/25/2005 9 : 0 0 : 0 0 A M : G o t o s c h o o l 9 : 3 0 : 0 0 A M : B u y t e x t 1 0 : 3 0 : 0 0 A M : G o t o l a b 1 1 : 3 0 : 0 0 A M : H a v e l u n c h 1 2 : 3 0 : 0 0 P M : G o t o class D e t a i l N e w E d i t D e l e t e S t e p l : I C I C S S t e p 2 : X b u i l d i n g S t e p 3 : D E M P T o t a l d i s t a n c e : 5 0 m e t e r s T o t a l e s t i m a t e t i m e : 5 m i n tr CEME Cttteit Out Annex Trailer MacLeod 8u*t>ng &4 The Bnmacomc* BuWms(AMPEL) • > Institute for Computing ~ i i i and C o g ms 5 Figure 3.6: The prototype for displaying a high-level path Displaying Low-Level Paths Note that the high-level path does not show the steps down to room level. The user may not know the exact procedure for arriving at the "To" destination, but he/she has already acquire an overview of future routing. To view the fully detailed path, the user can click the "Detail" button. Not only the path but also the estimated total distance and time are shown. Here, we need the input of the client's speed. The administrator can set the normal speed of the client and the speeds of elevators and buses. We use the same interface icons to display both high-level paths and 19 low-- leve l p a t h s . Today is: 11/25/2005 9:00:00 A M : Go to school 9:30:00 A M : Buy text 10:30:00 A M : Go to lab 11:30:00 A M : Have lunch 12:30:00 P M : Go to class Detail New Edit Delete Detail: 1:00:00 PM: Go to class Fran: ICICS108 Location: DEMP101 (Alarm- 12:20:00 PM] Step I Step2 Step3 ICICS10S ICICS104 ICICS200C Total distance: 50 meter(s) Total estimate time: 5 mins F i g u r e 3.7: T h e p r o t o t y p e for d i s p l a y i n g a low-level p a t h 3.4 High-Fidelity Prototype I n t h i s s e c t i o n , we present several screen shots of the h i g h - f i d e l i t y interfaces t h a t are used as f i n a l s y s t e m interfaces. T h e y are done i n J a v a u s i n g its S w i n g package. A l l of the des ign r e q u i r e m e n t s a n d suggestions b y t h e evaluators are m e t , a l t h o u g h some aspects are n o t perfect , d u e t o i m p l e m e n t a t i o n challenges. F o r e x a m p l e , o n l y one a r r o w is s h o w n between t w o places. M o r e arrows c a n be s h o w n by s t o r i n g m o r e i n f o r m a t i o n . H o w e v e r , t h i s w i l l n o t o n l y require f u r t h e r i m p l e m e n t a t i o n w o r k , b u t i t w i l l a lso s l o w d o w n t h e s y s t e m . S i n c e t h e p a t h is presented h i e r a r c h i c a l l y , t h e d i s t a n c e b e t w e e n two same level a r c h i t e c t u r a l e lements t h a t are s h o w n w i t h i n the same p i c t u r e is s m a l l . T h e r e f o r e , one a r r o w is e n o u g h t o a l l o w users t o have a basic i d e a of r o u t i n g . 20 M e d i u m - F i d e l i t y P r o t o t y p e Evaluation 100 ~t . — — - ~ 90 \ Interface Functionality Ease of Use "Willingness for Satisfaction Satisfaction Future Use Figure 3.8: Evaluation of the medium-fidelity system prototype Figures 3.9 to 3.20 present the sequence of screen shots when accessing two main functions. Figure 3.9 is the startup screen. Three buttons are added besides the buttons for managing the scheduler. The administrator can use the "Map" and "User Info" button to view and change the map and user information, respectively. The user can use the "Reject Location" button to report his/her difficulty in ac-cessing the current location. The related functions will be discussed in Chapter 4. Clicking the "New" button triggers another window for adding a new event. Figures 3.10 and 3.11 are respectively two screen shots of the new frame when the infor-mation is not entered and when it is. Pressing the "Add Event" button in Figure 3.11 will make the action take place and display an information message, as shown in Figure 3.12. The main screen now will be as shown in Figure 3.15, with the new event listing at the end of the event list. Figures 3.12 to 3.14 are prompting messages to inform or confirm for the user that a certain action took or may take place. Figure 3.13 is triggered if the user selects an event from the event list and clicks the "Delete" button on the main screen. If the user really wants to delete that event, he/she can press the "Yes" button, and then Figure 3.14 is shown. If the user doesn't want to delete that event, he/she can press the "No" button. Nothing changes in this case. 21 After an event has just been entered, it is highlighted. If a path is found for that event, then the high-level path is shown. Note that the buttons that represent the corresponding (sub)destinations are located where the (sub)destinations are. Since nodes are not able to appear on the same picture, buttons for each picture should be added so that users can view any portion or the whole path. These buttons are placed on the left bottom corner, as shown in Figures 3.15 to 3.20. To view specific information about a place, the user can click the button representing that location. For example, if the user wants to view information regarding the X building, he/she can click the "2" button and screen Figure 3.16 will show. To view the detailed path, the user can click the "Detail" button, and then Figure 3.17 will show. Though the arrows are not aligned perfectly on the hallway, the user can easily find out the correct location. Some (sub.)destinations are not shown in this picture, but users can view the next picture by clicking the "Picture 2" button, resulting in Figure 3.18. Clicking on one of the buttons that represents (sub)destinations will display useful information regarding the desired (sub)destination in the left-hand pane. The result of an evaluation of the above configuration are shown in Figure 3.21. 22 Figure 3.9: A screen shot of the Route Planner when it starts 23 H>; Add New Event Si Subject: Start Pate: (mm/ddVyyy) Start Time:- (tih:mm:ss AMiPM) From: Location: Required Attendees: Reminder Time: (hh:mm:ss AMJPM) Add Event Reset Close Figure 3.10: A screen shot of the frame for adding a new event Add NewEveirif Subject: GOTO .CLASS Start Date: (mm/dd.iyyyy) 10/11/2006 Start Time: (hh:mm:ss AMfPM) 1:30:00 PM From: IQCS103 Location: DEMP1.01 Required Attendees: FteminderTime: (hh:mm:ss AM.PM) 1:20:00 PM Add Event Reset Close Figure 3.11: A screen shot of the frame for adding a new event, with information entered 24 Figure 3.12: A screen shot of an information message Confirm Deleting an Event iWouidyou like to delete ewntsiiaoiOO.RM^OiTOiGLASS? Yes No Figure 3.13: A screen shot of a confirmation message Figure 3.14: A screen shot of a confirmation message 25 ng "•The schedule is far IMldOOi. 9:00:00JUil:_Go_to_school 9:30:00Jtotjsuyjexts 10:30:00 _AM:_Go Jo Jab 11:30:00 _AM:_Have_tach 1230:00 _PMMXI Jorum 1:30:00_PM_OOJO_CLASS Detail E * Delete Map Usei Wo RHject Location "nWtamntpositioniiilClCSlOl] « Step 1: UBC ICICS •» Step 2: UBC _CSJi>> Step): UBC jDQn> Total distance is: 10.0 lueterts) Minimum distance ii: ItMl meterti) The estimated time is If.!) minute(s) Picture 1: J ICICS JJEMP To liew tile detail of a place, click its buton once. To set your positon to it, click its buton once again. Figure 3.15: A screen shot of the Route Planner when a high level path is shown 2G h Accessibility Routing •'•The schedule is far 1VU/2006. 9:00:00_AM:Jjo_to_school 9:30:00_ftM:_BuyJexts «30:00_AM:j;o_toJaf] 11:30:00 JlM:_Have_lurich 12:30:00_PM:_LCI Jorum 1:30:00JW.J30 10 CLASS 1 Detail New Delete j Map User Info Reject Location * * *Your rumntp» j i t ion i * : IC ICS lQ8 * * * Stepl i l IBCICICS => M r ? 2: UBC_CS_X-> Step 3: UBC_DEMP Total distance it: 10.0 meter*!) Minimumdistance is: Ib.i) rncirrts) The estimated rime is 15.0 minute(f) t1K_CS_X(hSouthEasr)isa anewfcuilding &r the Department of Computer Science. LJ ICCS.DEMP To view the detail of a place, click its burton once. To set your position to it, click its button once again. F i g u r e 3.16: A screen shot of t h e R o u t e P l a n n e r w h e n t h e user wants t o v i e w t h e l o c a t i o n i n f o r m a t i o n of a place 2 7 t » Accessibility Routing *** The schedule is far: WU/2006. 9:00:00Jua:_GoJo_scliool 9:30:00_AM:_BuyJext8 10:30:00JW*_Go Jo Jab 11:30:00JUAJlaveJuncfi 12:30«0_PM:_LCI Jorum 1:30:00 PM: GO TO CLASS Close Detail New Edit Detet Map llst'i Info Reject Location Your rurrent position is: ICTCS108 «** 1:30:60 PM: GO TO CLASS From: ICICSW8 Ucation: DEMP101 (alarm: 1:20:01PM) Step 1: IC1CS1W •> Step 2: ICICS104 -> Step 3: ICICSelev2_l-> Step 4: ICICS200t -» Step5:CS_X209c-» Step o: CS_Xelml_2 -> Step T: CS_Xeler2_2 -> Step «: CS_Xele»2J •» Step »: CSXdoorT "a Step 10: DMNooil w S»pll:DBlP101 Total distance is: 42.0 meteits) \luuiHnni diltanre is: 42.0 meterts) The estimated time is f rninuteO) Picture 1: ICICSflooM Picture t. Picture 3: • idcsnoon To view the detail of a place, click its burton once. To M-i voni position ro ii.cli.rk its button once again. Figure 3.17: A screen shot of the Route Planner when a low level path is shown 28 w Accessibility Routing *** The schedule is for: 10/11/2006. " 9:00:00JUtl:_Go_to_scliool 9:30:00_AM:._Buy_te>rts ! 10:30:00._AM:..GoJo.Jal> i 11:30:00_AM:_HawiJuncli 12:30:<IO_PM:JXIJorum ; 1:30:00_PW:_CO_TO_aASS Close Uetail New Edil Deist Map User Info Reject Location *** Your run* jit position ir: ICICS1M *** j l:30:»0PM:GOTOCLASS From: 1CICSI08 Union: DEMPI01 U U I I M : 1:20:00 M ) Stop I: ICICS108 -> Step2:ICICS104-> Step J: IClCSelevI 1 •> Strp 4: ICICS200< SfeffcCS.XMc •> Slop ii: CS Xrlrrl 2 -> Sttp 7: CSJW«r2_2 -> Sfcp8: CS_Xflr«2_l -> Step 9: CSXdoorT •» Slop 10: DEMPdoorl »v Srtp UiDEMPlOl Total distance is: 42.0 meterii) \Iuumuiii distance i»: 42.0 nieterts) Thf estimated time is 5 miliums) Picture 1: ICICSfloorl Picture Z CS_Xfloor2 Pirtute3: CS_Xflouf1 • I JI I .'...JT CS Xllcoi? SB To set your position to it, click its button once again. Figure 3.18: A screen shot of the second picture of the low-level path 29 h Accessibility Routirv *** The schedule is for; W/Uaoot. 9;Orj:00_»M:_Go_to_sdiool 9.30:Q0_AM:_Buy_texts 1M0:00_AM:_Gn_toJiib 1 l:30:00_«M:_Have Jutich 12-.30:00_PM:_LCI Jorum 1:30:00_PM_GO_TO_CLASS Close Detail '] New i Edit ; Unlet Map User Info Reject Location Your tumntposition is: ICICS108 ***• 1:10:00 FM: GO TO C U S S From: ICICS108 Location: DEMP1D1 (alarm: 1.20:00 M i S tep l : lC ICS108 -» Step2:ICICS104 •> Step J: ICICSelrv2 l - » Step 4: ICICS200r •• Step 5: CS_X200c •> Step6:CS_Xele« l_2» Step7:CS_Xelev2_2-> Step «: CS Xelcul 1 - » Step »: CSXdoorT Step 10: DEMPdoorl - » Stepll:DEMP101 Total distance is: 42.0 meteits) Minintum distance is: 42.0 nieterts) The estimated time is 5 minutels) Picture 1: ICICSfloorl Picture 2: CS Xfloor2 Picture 3: CS Xfluur 1 ;J cs..xnoori To view the detiii of a place, click its burton once. To set y»ur position to it, click its burton once again. Figure 3.19: A screen shot of the third picture of the low-level path 30 h Accessibility Routing 9:00:00_AM:_Co_to_scliool 9:30:00_AM:_BuyJeilts 10:30:00 _AM:_Go_to_lali 11:30:00 _AM:_HaveJunch 12:30:00_PM:_LCIJOlum 1:30:00 Pf* 60 TO CLASS - : • •use Detail New Map \ User Info j ; Reject Location ***Yo»rcumritpositMis:ICICS108 * " 1:30:11PM: GO TO CLASS From:ICICSUI8 Location: D£MP]01 (alarm: 1:20:00 PM) Stt»l:lClCS10!-» Step 2: ICICS104=> StepJ;iaCSelev2_l-» Step 4:1C1CS2O0I -> Step 5: CSjeOlc:. Step li: CS Xelrvl 2 •> Step 7: CSXektfJ »> Step !: CS_Xelev2_l => Step«:CSJidoer7-> Step 10: DEMPdoorl •» Stepll:DEMP101 Total iluiamr it: 42.0 metelts) Minimum du tanee if: 42.0 meterts) The estimated time is 5 nunutcts) Picture 1: ICICSflooM Picture 2: Picture 3: Picture 4: G DEMPfloort Agronomy Road ML... T To view the detail of aplace, click its button once. | To ';<M v'nti position to it, click its button once again. Figure 3.20: A screen shot of the fourth picture of the low-level path High-Fidelity Prototype Evaluation Interface Functionality Ease of Use Willingness for Satisfaction Satisfaction Future Use Figure 3.21: Evaluation of the high-fidelity system prototype 31 Chapte r 4 Functionality 4.1 Scheduler and Reminder The scheduler is based on a list of events, which can be imported from Microsoft Outlook. Each event item contains three mandatory attributes, namely "Subject", "Start Date" and "Start Time", and several optional attributes. A Java class Trans-ferSchedule, class is implemented to read the file containing the event list and to transfer the list to an X M L file. The X M L file is then parsed and several lists are created. These lists include a list of event summaries that are shown in the main screen, a list of reminder times that are compared with the current time, and a list of details that contain all the information the user entered as well as path detail, if a path is found. With these lists, the implementation is simplified and the following functions are easily achieved. 4.1.1 B a s i c Schedu le r Func t i ona l i t y Since the event list is transferred from Microsoft Outlook, many functions provided by it can be used to supplement functionality of our scheduler. Therefore, our scheduler provides only several basic functions on managing the schedule, based on viewing or modifying the lists that are created when the Transfer Schedule, class 32 parses the schedule list. Adding a new event adds an X M L item to the X M L file in a sorted order. Then, the Transfer Schedule, class parses the X M L file again, so all lists are also updated. To insert the item in sorted order, we suppose that the original list is sorted and that we can linearly search for the correct position. Since the number of events in a one-day schedule is usually small, linear search is appropriate. To delete an event, the client selects the event name from the list and presses the "Delete" button. An index number i is obtained when the client selects the event. The i-th items of the event summary list, reminder time list and detail list are removed from those lists. Editing an event's information is equivalent to deleting the event and then adding another one. Therefore, when the client wants to edit an event's information, it is displayed in a window similar to that used for adding a new event (see Figures 3.10 and 4.1). If the user presses the "Edit Event" button, the event item is deleted first, and then an new event with the information filled in, as shown in Figure 4.1, is added. When the client presses the "Detail" button from the main screen, the high-lighted item's index number i is obtained. Then the i-th item from the detail list is shown. 4.1.2 Reminder Function for Individual Clients Our system supports both manual reminder and automatic reminder functionality. The manual reminder time is obtained when the client creates an event item, while the automatic reminder time is calculated by the system for each individual client based on his/her speed. A thread is created to act as a time counter. It reads the system time every second and compares it with each reminder time in the lists. If the system time matches one of the manual reminder times, the system will prompt with a simple message, as shown in Figure 4.2 (a). 33 & Edit Event Subject: pOJO'pLASSl • Start Date: (mm/ddtoyy) 10/1172006 Start Time: (hh:mm:ss AMPM) 1:30:00 PM From: ICICS108 Location: D.EMP1 01 Required Attendees: Reminder Time:; <hh:rhm:ss; AMJPM)' 1:20:00 PM Edit;Event Close Figure 4.1: The frame for editing an event's information Message ® REMINDER: 1:30:00 PM: GO TO GLASS DK! (a). Figure 4.2: (a) A prompted message for manual reminder; (b) A prompted message for automatic reminder 34 ^Mqdifymew/lnput... User's position: ICIGS1 OS-User's speed: 10.C User's difficulty: 0.0 Save Close Figure 4.3: The frame for inputting user's information When the system is launched, the administrator or the client himself/herself can enter some basic client information into the system database. This information can be learned using Machine Learning methodologies, such as Logistic Regression and Particle Filtering. For simplicity, our system will take the needed information as inputs (see Figure 4.3). For each event, if a path is found to the destination, the required time for the client to arrive there is calculated as follows: distance , required time = . (4-1) speed The required time is rounded to minutes and subtracted from the event time. The result is the automatic reminder time, and is compared with the system time as well. A automatic message is shown, as Figure 4.2 (b). 4.1.3 C o o p e r a t i o n be tween Schedu ler and R o u t e P l a n n e r From the above discussion, it can be noted that the scheduler and the route planner cooperate closely. This section summarizes the cooperation between them. The scheduler handles all the event information. The destination list is maintained by the scheduler. When the information is updated, the scheduler checks to see if the destination has been changed. If it has, the scheduler corrects the destination list. With this list, the route planner can find a suitable path and estimate the distance. 35 Therefore, the required time can be estimated as well. Based on the required time, the scheduler provides an automatic reminder to each individual client. 4.2 Hierarchical Shortest Pathfinding The notation used in this section is described here. A map is represented by a graph G = (N,E,l), where N is the set of nodes, E is the set of edges, and I is the number of hierarchical levels. The cardinalities of N and E are denoted by n and e. Each node £ N contains a set of neighbours neigh(node) and is as-signed to a level. Each edge & E is associated with another pair of nodes {exit, entrance) where exit and entrance are nodes of one level lower, or they may be null. The weight on an edge is denoted by w(i,j), and the weight of a path P is w(P) = J2(i w(i, j)- We want to find the shortest path P that has the minimum weight w(P) from a node s to another node ci in a graph G. Definition 1: Search on a graph G: The process of finding a path P — {s,i, ...,d}, a sequence of nodes on G, from the start s to the destination d. Human beings may approach complex problems by dividing them into easier sub-problems, each of which can be further divided into smaller problems or solved by a quicker search. More than forty years ago, Minsky realized that a successful division of a complex problem will greatly reduce the search time by a fractional exponent [19]. Such divisions can be considered as "islands" in the search space, where these islands can be abstracted from groups of nodes. In this section, an abstraction of a group of nodes is found when these nodes are at the same level and within the same enclosure. The abstraction then is their enclosure. For example, a building is an abstraction of the rooms in it. Conversely, details at the room level constitute the refinement of the building. 36 Definition 2: Hierarchical Pathfinding: The process of finding a sequence of paths {P m , P m _ i , P o } , m < I, where Pi contains nodes of i-th level and that are an abstraction of nodes on Pj_i. The maximum length of an abstract path then is defined as maxlength{Pi) — w(Pi) + Y^nodeaP upperbound(node), and the minimum length of the path is minlength(Pi) = w(Pi) + YlnodeePi ^°werbound{node), where upperbound{node) is the maximum of shortest distances between any pair of places within the node and lowerbound(node) is the minimum distance from the entrance to the exit. The upper bound of a node can be overestimated, whereas the lower bound can be underestimated. Hence, upperbound(node) can be the total of low-level edge lengths in an abstracted node, node, and lowerbound(node) can be zero. 4.2.1 Previous Work One of the well-known search algorithms is Dijkstra's, a non-heuristic version of A*. However, it is not as efficient as some A * algorithms with good heuristics. A * [10] and IDA* [13] are both heuristic and complete algorithms on locally finite graphs (graphs with a finite branching factor), but neither of these two algorithms is well-suited into a dynamic environment. Hierarchical pathfinding has been explored since the mid 90's, and several excellent algorithms have been developed. In [24], Rabin provides a high-level de-scription on path-finding using a two-level hierarchy. This algorithm uses only two levels of abstraction, but real world maps are divided into numerous levels. In [11], Holte et al. explain how abstraction could lead to speedup on finding a solution for a graph-oriented problem. Similar to their work, Hierarchical Pathfinding A * (HPA*) [8] and Partial-Refinement A * (PRA*) [28] both construct multi-level abstractions using grid-based representation, and greatly reduce the amount of time required to find a near-optimal solution. Instead of estimating a near-optimal solution, our 37 algorithm finds all possible candidates and keeps them until some are proven to be non-optimal. If the number of sub-level nodes of each node is fixed, the size of the map increases exponentially as the number of levels increases. The running times of existing search algorithms grow exponentially as well, so they may not be practical in reality. In this thesis, we propose the Hierarchical Shortest Path (HSP) algorithm, as well as a hybrid of the HSP and A* (HSPA*) algorithms. HSP finds a threshold that is the least upper-bound of the length of a high-level path, and refines the paths that may be shorter than this threshold. It stops refining a path when the path length exceeds the threshold, and finally returns the shortest path among those that remain. HSPA* works in a similar way, except that it uses A * for refinement as soon as it reaches a specific level. The experimental results show that HSP and HSPA* are suitable for various applications. Figure 4.4: Examples: (a) and (b) are maps of campuses comprising three and four buildings, respectively, where each building contains a number of rooms 4.2.2 The A l g o r i t h m The hierarchical approach taken in this section involves multi-level representation of a real-life map. For example, these levels can be campuses, buildings, floors and rooms. Assume that a person wants to travel from room R\ in building B\ to room i?2 in building B2 in the world of Figure 4.4 (a). If the person begins his pathfinding from B\ to B2, then he could easily find the shortest path from B\ to B2 with one edge. If the walking distances within these buildings are negligible, then the person can take the shortest path between B\ and B2, and needs only to refine the path 38 from Ri to the exit of B\ and the path from the entrance of i?2.to R2. Hence, the person does not need to search paths within B3. Therefore, in order to save time in planning, we can search for short paths between high-level sites first, and then refine the paths within the selected sites for more details. However, the high-level shortest path does not guarantee the shortest overall path, because the paths within each site can vary in length. Consider the previous example, where the walking distance within buildings B\ and B2 is significant. Then, a path from Ri to the exit of B\, and a path from the entrance of B2 to i?2, may increase the length of the overall path by a large amount. As a result, we can prune away a path P only when it is guaranteed to be non-optimal, i.e., the minimum length of the path exceeds the maximum length of another path: 3P' : minlength(P) > maxlength(P'). (4.2) The main algorithm, 4.1, consists of four major steps: first, find the topmost level abstract nodes wherein the start node and the destination node are different, and abstract the graph G to that level (line: 1,2); second, find a high-level shortest path between ancestors of s and d at the l-ih level, based on which a threshold is obtained (line:3,4); third, find all possible high-level paths with minimum lengths less than the threshold (line:5); finally, refine each high-level path to the lowest level and return the one with minimum length (line:6,7). This algorithm is complete since it maintains all possible paths and ignores a path only when it is definitely longer than another possible path. The following will explain the algorithm in further detail. Abst rac t of a Graph : As mentioned above, the desired level in the first step is needed for abstracting the graph. For example, if a person travels from a room in a city to another room in another city within a country, the two cities are the topmost places he should consider. In other words, we seek out the first same ancestor of the two places to find the one level below that ancestor that is desired. Algorithm 4.2 presents the procedure for finding the first same ancestor of two places 39 Algorithm 4.1 : main (G,s,d) Input: A graph G = (N, E, I), the start node s and the destination node d Output: A shortest path P from s to d 1: Z := same Ancestor Level(s,d) — 1 2: G :— abstract(G,l) 3: P := A*(G, ancestor(s, I), ancestor(d, I)) 4: threshold := maxlength(P) 5: S := possiblePaths(G, ancestor (s, I), ancestor{d, I), threshold) 6: S := refine(S, I, threshold) 7: Return P :— minis'} Algorithm 4.2 : sameAncestorLevel (s, d) Input: The start node s and the destination node d Output: The level I' of the first same ancestor of s and d 1: lower — lower Lev el Node(s, d) 2: higher = higher Level Node(s, d) 3: while lower is not at the same level as higher do 4: lower = parent(lower) 5: end while 6: while lower is not a sibling of higher do 7: lower = parent(lower) 8: higher — parent(higher) 9: end while 10: Return Z' := level(lower) and returning the level of that ancestor. Note that pathfinding between different level architectures is also allowed. After the desired level 1 is found, the graph G is abstracted to that level: G:=(N,E), (4.3) where and E are subsets of and E, respectively, and the nodes node € iV and iyj '• {i>j) £ E are only those at level I. A High-level Shortest Path and a Threshold: Either Dijkstra's or the A * algorithm is used on G to find the shortest path P between the ancestors of s and d at the Z-th level. Then, maxlength(P) is the threshold that we are looking for. The time required to find the high-level path and the threshold is insignificant if the number of high-level nodes is relatively much smaller than that of low-level 40 Algorithm 4.3 : possiblePaths (G, ancestor(s, I), ancestor(d, 1)) Input: The abstract graph G, the ancestors of s and d at level /, and the threshold t Output: A set of paths at level I with minimum lengths less than t 1: CurrentPaths <— { {ancestor(s, I)} } 2: Output <— { } 3: while CurrentPaths changes do 4: for each P £ CurrentPaths do 5: if last node In of P is ancestor(d, I) then 6: Output <— Output U {P} 7: PossiblePaths <— PossiblePaths U {P} 8: else 9: for each nb £ neigh(ln) do 10: P = P U {n&} 11: if minlength(P) is smaller than t. then 12: PossiblePaths «- PossiblePaths U {P} 13: end if 14: end for 15: end if 16: end for 17: CurrentPaths <— PossiblePaths 18: end while 19: Return Output nodes. High-level Possible Paths: After a threshold is obtained, we could search on G for all possible high-level paths and prune away those with minimum lengths exceeding the threshold. This process is shown in Algorithm 4.3. Hierarchical Refinement on High-level Short Paths: After high-level paths are found, a refinement step is executed, as shown in Algorithm 4.4. It refines each possible high-level path to one level lower each time by recursively finding the shortest path from the entrance to the exit of each node on the high-level path. Then, the lowest level path is constructed by concatenating all partially refined paths. The refinement step ceases if the length of the current path exceeds the threshold. 41 Algorithm 4.4 : refinement (S, I, t) Input: A set 5 of high-level paths, the lowest level I and the threshold t Output: A set S of low-level paths at level I l:S = {} 2: for each hierarchical high-level path P £ S do 3: R = {s} 4: for each node £ P do 5: find the edge from last node of P to the entrance of node 6: if lev el (entrance) > I then 7: P' = HSP'(entrance, exit) where entrance and exit are within node 8: P = PUP' 9: end if 10: if minlength(P) > t then 11: ignore P and continue the outer loop 12: end if 13: end for 14: S = SU{P} 15: end for 16: Return S 4.2.3 A Hybrid of H S P and A * (HSPA*) Note that there may be more than one high-level path of shorter length than the threshold, but that is unusual in many realistic cases. The refinement step is fast, if the number of possible short paths is small. Otherwise, the time spent on refining these paths may be excessive. Hence, we could refine our algorithm to select between HSP and A * depending on the number (a) of these paths. Furthermore, from the experimental results shown in the next section, it is evident that HSP is not as fast as A* if the number of levels is small and the number of sub-nodes is not significantly large. Therefore, we could use A * algorithm when a high-level path is refined to a specific low-level (f3). 4.2.4 Analysis of Running Time Let b be the number of sub-nodes within an abstract node. If b is fixed, then the total number of lowest-level nodes is n — bl, where I is the number of levels. A* and 42 Algorithm 4.5 : hybridRefinement (S, I, t, s, d) Input: A set S of high-level paths, the lowest level I, the threshold t and the start node s and the destination node d Output: A set S of low-level paths at level I 1: if IS1) < a then 2: S = {} 3: for each hierarchical high-level path P 6 S do 4: P = {s} 5: for each node £ P do 6: find the edge from last node of P to the entrance of node 7: if lev el (entrance) >l+[3 then 8: P' = HSP (entrance, exit) where entrance and exit are within node, 9: else 10: P' = A* (entrance, exit) 11: end if 12: P = PUP' 13: if minlength(P) > t then 14: ignore P and continue the outer loop 15: end if 16: end for 17: S = SU{P} 18: end for 19: else 20: S = {A*(s, d)} 21: end if 22: Return S IDA* with good heuristics can solve difficult problems in polynomial time [27, 14], i.e., 0(nc) = 0(bd) where c is a constant. Therefore, the algorithms are exponential to the order of I. On the other hand, HSP only executes searches on 0(b) nodes on each level if there is only one abstract path found. In this case, HSP has a running time of 0(bcl). Even if there is more than one abstract path, say d paths, the running time of HSP is 0(bc dl). As long as d <S b, HSP is still much more efficient than A * or IDA*. In reality, where distances within a site are usually considered insignificant, one high-level abstract path is expected to appear. Hence, the running time is 0(bcl) for the HSP algorithm in real-world examples versus 0(bcl) for A * and IDA*, i.e., linear in I versus exponential in I. Moreover, I is usually small while 43 b is large. Thus, Q(bcI) or 0(bcdl) should be relative smaller than 0(bcl). 4.2.5 C o m p a r i s o n and R e s u l t s In real life, each site is connected to its neighbours from exits to entrances. For example, if two buildings are adjacent, then we can exit from a door of one building, and enter through a door of the other building. Hierarchical maps in our experiments are constructed with this connection, in mind. Weights of the edges and the upper bound of the shortest distances between any pair of places within a site are included in the map, which is created in X M L form. One of the reasons that we use X M L form is due to its consistency and flexibility; i.e., you have to define each element with an opening and an ending tag, while you can use an arbitrary tag name. Another reason is that nested tags in X M L can represent hierarchical relations easily. A l l experiments described in this section were run on a Linux Intel(R) Pen-tium 4 machine running at 3.20GHz with 2 GB of R A M , using Eclipse version 3.1. Examples First, we analyze the algorithms using the example shown in Figure 4.4 (a). Each building contains three floors, while each floor contains three rooms, and so on. In reality, the number of nodes at one level lower is usually much more than three, where the time saved from pruning away unnecessary high-level paths is more obvious. Here we use this example because of its simplicity in building an X M L file and its simulation of a simple world. The total number of lowest level nodes is no = 3', and since it grows exponentially to the order of I, Dijkstra's and A * algorithms are inefficient here. The A * heuristic used in our experiments is the Euclidean distance, which is popular to use in real-world route-finding problems. However, this heuristic may not be useful for a multilevel structure, since the heuristic values for low-level nodes may be similar. For example, the Euclidean distances from nearby rooms in one city to a room in another city may be indistinguishable. In contrast, HSP, which prunes away most insignificant high-level paths, runs well in these examples. As shown in Figure 4.5 (a) and (b), both multi-level representation running 44 times of Dijkstra's and A * algorithms blow up quickly, while the HSP running time grows slowly. A semi-log graph of the three algorithms' running times is drawn in Figure 4.6 (a). We can observe that the slope of the HSP curve is significantly less steep than that of the other two curves. A further observation from Figure 4.6 (a) is that HSP is not superior among the three algorithms all the time. When the number of levels is small, HSP wastes time on recursive calls. Therefore, it is better to use a hybrid of HSP and A*, the HSPA* (as described in Section 4.2.3). The running-time results for HSPA* are compared with those of the other three algorithms in Figure 4.6 (b). Note that HSP and HSPA* have similar performances in these examples, although HSPA* is more stable. Table 4.1 shows the running times of the four algorithms in the example where every abstract node has four sub-nodes, as shown in Figure 4.4 (b). In this example, more than one abstract high-level path is found at each level, but the running times of HSP and HSPA* are still promisingly good. Examples with larger number of sub-nodes (b) are also explored, and the outcomes are shown in Table 4.1 as well. ( a ) L e v e l :1 - 5 ( b ) l _ e v e l : 5 - 8 D i j k s t r a A * HSP J / L e v e l L e v e l Figure 4.5: Running time plots: (a): the running times of three algorithms from level 1 to 5; (b): the running times of three algorithms from level 5 to 8 45 (a) 3 a l g o r i t h m s ' r unn ing t ime (b): 4 a l g o r i t h m s ' r unn ing t ime B 1 ° L e v e l 2 1 0 zzz CC L e v e l Figure 4.6: Running time plots: (a): the running times of three algorithms in log-scale; (b): the running times of four algorithms in log-scale 4.2.6 R o u t e P l a n n e r for P e o p l e w i t h D isab i l i t i es Our route planner, which shows accessible routes for wheelchair users, contains data of several buildings at U B C : ICICS, the X building and Dempster, the three main buildings in our department. Based on an accurate schedule, the destination can be easily estimated. Then, the route planner computes the possible paths to the destination using HSP and shows both a high-level and a low-level path to the client. Figure 3.15 shows a high-level path from ICICS to Dempster, where most classes are held. After the user clicks the "Detail" button, the low-level path corresponding to the high-level path is shown, as in Figure 3.17. 46 Table 4.1: Running times: The running times (in milliseconds) of four algorithms using examples where every abstract node has four or more sub-nodes (b) (b): (4) (5) Level: 1 2 3 4 1 2 3 4 Dijks t ra 2 6 35 295 2 11 94 1278 A * 1 4 22 149 1 10 56 717 H S P 1 8 23 130 2. 12 42 97 H S P A * 1 8 24 174 1 11 35 88 (6) : (6) (7) Level: 1 2 3 4 1 2 3 4 Dijks t ra 3 24 350 7817 3 25 466 15601 A * 2 23 218 3462 2 21 231 8368 H S P 2 28 131 354 2 31 137 368 H S P A * 2 24 99 296 2 28 116 316 4 . 3 Alternative Path for Individual Clients In this section, we will explain the design and method used to recalculate an al-ternative path and display it to the client. Users can simply declare to the system that the current location is too difficult for them to get through. In the next run of pathfinding, the system will not generate a path with higher or equal difficulty to the rejected location. Although we just use a simple computation to eliminate difficult locations for each user independently, we could apply sophisticated machine learning methods to train the model representing the user's ability. This approach is considered as future work. 4.3.1 Re jec t the C u r r e n t L o c a t i o n If a place is difficult to get through, the weights of edges connected to it are increased by some portion: w= w 4.4 access (pj) where e — (x,p) and access(p)is the accessibility of the place. This technique works for all clients. However, different people have different strengths, so the system should be adaptive to individual users. 47 Assume that a student from LCI uses the Scheduler and Route Planner system and that he/she wants to attend his/her class in Demspter room 101. He/she follows the path the system suggests and arrives at the hallway between ICICS and the X building, as shown in Figure 4.7. He/she then finds out that the hallway is actually a ramp, which is hard for him/her. By clicking the "Reject Location" button, the student can let the system know that a node on the path exceeds the acceptable difficulty level of the user. A confirmation message, as shown in Figure 4.8, is displayed after this action. Then the system verifies that the location is more difficult than the assumed difficulty the user can accept, and updates the information of the user. From this point on, whenever the route planner tries to find a suitable path for the current user, it tests whether a place is acceptable to the user. If not, it ignores it; otherwise, it runs as the usual HSP or HSPA*. 4.3.2 Recalculate a New Path After the user rejects the location, he/she can obtain a new path from his/her previous location to the destination. First, the user can roll back to the previous step by double-clicking on the previous button. Then he/she can edit the event information, and change the "From" location to his/her current position. He/she can simply remove the text in the "From" field, and the system will assume the start location is where the user currently is, as shown in Figure 4.9. Figure 4.10 shows the screen after the user finishes editing. A new high-level path is found, as well as a low-level path. Figures 4.11 to 4.13 show the sequence of screen shots of the system, displaying different pictures of the low-level path. 48 Table 4.2: An example of an event item in X M L element form <event> _ ' ... <Subject>Go to school</Subject> <Start Date>ll/25/2005</Start Date> <Start Time>9:00:00 AM</Start Time> <Reminder on off>8:50:00 AM</Reminder on off> <Location>#99busstop< /Location> </event> 4.4 Database Management In this section, we will discuss database management for schedule operation and map representation. This is a significant issue, because our scheduler is based on event information data and the route planner establishes paths over the map database. The event information database is supposed to be of a small size, but the manage-ment of it still needs great care since many functions can access it. On the other hand, the map database is huge. Section 4.2 discusses the methodology that we use to reduce the time needed to compute a path. Here, we focus our discussion on data storage and modification. 4.4.1 Schedule Importing and Expor t ing There are three ways to create a whole day's schedule. First, the system can start with an empty event list, and the client adds event items one by one. Second, the administrator or the client can create an X M L file that contains events in the form shown in Table 4.2. Third, the administrator or the client can input the event information in Microsoft Outlook and export a text file, which can be imported into our system. When one launches Microsoft Outlook and selects "Calendar" on the bottom left corner, a time table appears on the right-hand side. Clicking on a time slot, a new window for creating an appointment appears, as shown in Figure 4.14. This scheduler includes more functions than our scheduler does. For example, the user 49 can schedule a meeting with his/her lab-mates and the Microsoft scheduler will automatically send email to all attendees. Rescheduling the meeting time is also allowed. If the client using our system prefers Microsoft scheduler, he/she can keep it on and use our route planner to find paths. After a list of events is entered, the subjects and destinations of each event are shown on the time table (see Figure 4.15). Selecting "File" from the menu bar, and then selecting "Import and Export" from the drop-down menu, will trigger a sequences of screens as shown in Figure 4.16. Following these screens, a file named "schedule.txt" is created. This file is a list of event information, in the format that our system accepts. Our system can export a similar text file, and Microsoft Outlook can import it by similar steps as explained for exporting. The Add, Delete and Edit functions all modify the data of the schedule, as explained in section 4.1.1. They modify the lists created by Transfer Schedule, class. This class is implemented to transfer the lists back to an X M L file, and then this updated file can be transferred back to a text file. 4.4.2 M a p Rep resen ta t i on In our map database, architectural elements are stored in a hierarchical manner. A campus contains a number of buildings that in turn contain a number of floors. Rooms are considered to be the lowest level architectural element in our system. They are contained in floors. Table 4.3 is an example of an X M L element repre-senting a campus with sub-elements (nodes) representing the architectural elements contained in the campus. Each architectural element has its own neighbourhood and location. This information is represented by nodes as well. Other information can also be stored, such as the difficulty of accessing the site and other detailed information regarding the place. In reality, the number of buildings in a campus is large, and there may be numerous rooms in each building. Therefore, the map database can be too 50 large to maintain and update. A Java class ConstructMap. class is implemented to parse the map X M L file and create lists of architectures. Then, neighbours are connected by edges, with lengths specified in the X M L file. Given the name of a place, ConstructMap. Class can look it up from the lists that contain all archi-tectural elements. ConstructMap. Class also provides method for adding elements representing new places into the existing map X M L file. It requires another X M L file containing the new elements in the same format as in the example in Table 4.3. ConstructMap. Class will find out whether a campus or other architectural element is added, and connect the new place with its neighbours. The user can also modify the existing map information by clicking the "Map Info" button from the main screen. A frame for modifying and viewing map information is then displayed, as Figure 4.17. There are four levels of architectural elements for this application. More levels can be added by an easy implementation. After clicking the "Select a campus" or other buttons, the drop-down menu on the left-hand side is activated, so the user can select the place that he/she wants to modify or view. Figure 4.18 shows the resulting screen for displaying the information for the U B C southeast campus. 51 Table 4.3: A simplified example of the representation of a campus in X M L form <camp name—"UBC southeast" > <camp neigh name="UBC southwest" > <distance>100.0</distance> </camp neigh> <x min>200</x min> < build name="UBC D E M P " > <build neigh name="UBC ICCS"> < build neigh > <floor name="DEMPfloorl"> < floor neigh name="DEMPfloor2"> </floor neigh> <room name="DEMPl01"> <room neigh name—"DEMPdoorl"> </room neigh> </room> </floor> </build> </camp> 52 > Accessibility Routing 11:30:00 J<M: Jiaue Junch 1230:00 .PMLLCI Jorum 3:47:00 PtVt GO TO CLASS Close LletatJ Map User Info blew Met Reject Location **' Your currentposuion it: KKS2IIIU *** M7:00 PM: GOTO CUSS From: ICICS1H8 Location: DEMP101 (alarm: 3:45:111 PM) Step 1: ICICSlOrj -> S*j2iICICSll4"> Step3:IClCSelev2 J => Step4:ICICS2M(^  Step 5: CS.X2H) •> Step »: CSXelevl 2 »> Step 7: CS_Xelev2_2 -» Step !: CS_XeIev2 1 •> Sttp9:CS_Xdo.t7-> Step 10: DFJUPdoorl -s Stepll:DEMP101 Total distance is: 42.0 nieterts) Minimujiidistance is: 42.0 meteris) TTie estimated time is 5 tiiinute(s) ICICS200c(mHTCSflooti)isa ahallway from CS building to tile Xbuilding. It's also a ramp with moderate slope. Pictuie 1: ICICSfloorl Picture 2: Picture 3: Picture 4: CS Xfloor2 OEMPrlourt • ICICSfloorl To view the detail of aplace. clirk its button once. To set your position to it, click its button once again. F i g u r e 4.7: A screen shot of t h e s y s t e m w h e n t h e c l ient arr ives at a c e r t a i n p o s i t i o n Message '1111111^^  -< '—~~~~~~~ You have declared that ICIC 3200c OK s too difficult for you to get through. F i g u r e 4.8: T h e re ject ion message i f t h e cl ient c l icks the "Reject L o c a t i o n " b u t t o n 53 ip Accessibility Routing 10:30:00JIM; Jio Jo Jab 11:30:00 _AM:JraveJunch 12:30:00J>M:JCI Jorum 3:4?:0U_PM:_GOJOJ;LASS Close Detail New Man User Info Youreumntpositionb:ICICSelev2J Edit Event Sutler* 3:41:00 PM: GO TO CUSS From: ICICSI08 Location: DEMP10I (alarm: 3:45:00 PM) Step hlClCSIM".-Step 2: ICICS 104=* Step ): ICICSekv2J -> Step 4: ICICSIUOc •> Step 5: CS_XM0c-> Stepo:CS Xelevl_2-> Step7:CS _Xele.2_2» Step»:CS_Xelev2J »> Step «:CS Xdoor7«> Step 10: DEMPdoorl •» Step UiDEMPlOl Totaldistance is: 42.0 mclerts) Minimum distance is: 42.0 me tens ) The estimated time is 5 minutefs) ICICSelev2 1 (in ICICSfloorl! it a an elevator on the first floor of CS building. Picture 1: Picture 2: Picture 3: Picture 1: To view the detail olaplace, click its button once. To set your positiou to it, click its button once again. Figure 4.9: A screen shot for updating information for an event bility Routing The schedule is far: 10/11/2006. 9:00:00_AM:_GoJo_scliool 9:3l):U0_AMLBuy_tBxts 10:30:00. AM: _Go Jo Jan 11:30:00 AM. Haw lunih 12:30:00_PM:_LCI_lotum 3:47:OfJ_PM:.GO_10_aASS Detail Hew ! m Oalete . Map ysei Into ((eject Loc5Jf^ _J ***Yoarrumntp8jitionu:iCICSelev2 1* Step I; TJBC_ICICS ^  StepMJBC DFMP Total distance is; 90.0 meters) Minimum diftanre ii: 90.0 meteris* The cjJ.in.aifd time is 17.0 niimitf(s) Picture 1: tCICS^OE-. ass CEME Annex Traitor MacLeod Buiktmg ! ! \ iaimm Ux Computing I j ; Cogn/lfVf Systems * Ttw Srimaccimbe Builfliri9(AMPEl) Landscape ArcriitftCturt Pulpand S p 3ap* Centre n l - ~ - * . - — — — - - L AGRONOMVaOAD Forest Soonest Centra Centre for Advanced Wood ProcesAtng To view the detail of a place, click its batten once. To set jour position to it, click its button once again. Figure 4.10: A screen shot of the system when a new high-level path is found 55 •kAccessibility Routing *** The schedule is far: 10/11/2006. 9:00:00JuYl;_Go_to_sr;hool 9:30:00JUYtJuyJexts 10:30:00JM:_Go Jo Jan 11:30:00 JIM: Jtavejuncti 12:30:00J>M:_LCI Jorum 3:47:00_P»t_GOJO_CLASS Closo Dew New Edit yap Uset info Reject Locdtim. *** YoiireiirrentpositionB: ICICSelev2 1 * »:47:»0FM:GOTOCLASS Location: DEMP111 (alarm: 3:45:00 PM) Stop l:ICICSelw2J-» Step I: ICICSelevl I Step3:iaCSelevl_m=» Step 4: ICICSlmindooi •> Step f: ICICSromer => Sttpti:DEMP4oorl Step DEMP101 Total distance be 114.6 meterts) Minimum Aistame is: 114.0 nine its) The estimated time is 12 minutefs) Picture 1: Picture 2: Picture 3: IClCSflooH C ICICSfloor! To view the detail oi'apUce,click its button once. To set your position to it, click its button once again. Figure 4.11: A screen shot of the system when it displays a new low-level path 56 •'Accessibility Routing •" The sctedule is/or: ttttWWi. 9:00:00_AM:_GoJo_school 9:30:00JtM:J3uyJe>(t.s 10:30:00. AM:._Go Jo JaB 11:3(l:00_4M:JHaveJunch 1Z30:00_PM:JCI Jorum 3:47:00J>m_GQ JO CLASS Close Qetail i yew : gdit Delete Map Us r Info Reject L ocation *** Yourrurrentposirion is: ICICSelev2 1 * 3:47:60 PM: GO TO CLASS Ucation:DMP10l (alarm: 3:45:00 PM) Step 1: IClCSel«2 1«> Step 2: IClCSelevl l-s Step3:ICICSelei<l_m-> Step 4: ICICSnvaindoor •> Step 5: ICICSromer=> Slep6:DB«Hul -» Step7:DEMP101 Total distance is: 114.0 meters) Minrmnmdistance is: 114.0 meterts) The estimated time is 12 minute(s) Picture 1: Picture 2: Picture 3: ICICSiltturl ICICSflourO DEMPftooil Agronomy Road m i To view the detail of aplace, click its buton once. To set your position to it, click its buton once again. Figure 4.12: A screen shot of the second picture of the newly found low-level path 57 'Accessibility Routing T3m "' The schedule is for: 10/11/2006. 9:00:00 J«M:j3rj_to_sr;ilool 9:30:00_AM:_Buy_teas 10:30:00 _AM:„Go Jojab 11:30:00 AM: Have Junch 12:30:00_PM:jXIJoruin 3:47:00 _PHl_GOJO.CLASS Close Qeiait New Edit Dl User Info Reiect Location **'YouriumMpositionis:ICICSelev2_l * 1:47:00 PM: GO TO CLASS Location: DEMPlfll (alarm: 3:45:00 PM) Stepl:ICICSelev2_l -> Step2:IClCSelevl_l -» Step 3: ICICSelevl JU' . . Step4:ICICSmauiQoor-J Step 5: ICICSromer-> Step 0: DEMPdoorl • Step 7: MMPlOl Total distance iv: 114.0 melerts) MirdmiiTi)dietanre it: IRQ meter**) The estimated time is 12 rninute(s) Picture 1: Picture 2: • J OEMPtloon DEMP110 washroom elev IX DEMP101 Agronomy Road • H To view Ike detail of a place, click its button once. To set yttur position to il, click its button once again. Figure 4.13: A screen shot of the third picture of the newly found low-level path 58 I J J c:«ti«Fi»i1ar M k i m t / l t Oirtlook ' b J i ^ l i S * I Cte »ow So look frtitxu, rjofc. C a l e n d a r < -tovsmber ;res •> 5 M T w I F S 1 8 9 4 1 November?: November 2? November 23 November 24 November 25 6 7 Q 9 10 11 12 13 14 IS 16 17 IS 19 8 " II II II 1 20 31 22 23 2* 25 26 27 28 79 30 rjacainbar 2005 goo 10 0 0 1 1 " [ • Fjto C * Daw [near! Fo/rnat tocta A/tkonc rjalp S M T W 7 P S 1 Z 9 ApporilTri»iV j ^hadulinrj^ • 11 12 13 14 15 16 17 10 19 20 21 22 23 Z* 26 26 27 28 29 30 31 1 2 " •it r • Go to school 1 r"ii My Cdtenctivt LofWon: *99buMtOO jjj ; • M m >*j 2 oo Startflmn; Frl 25/11/2005 v] 9:00 AW •*) F ] Al day. avenr 300 EndUr*: Frr 25/11/2005 v ( 9:30 AM " » ] l^Senunde' 15 rtxinur.es v j [^ 7j gir^t imeM • Bu«y v ! 5oo 6m 7™ g X , j T<Mkc 900 | r u M i f c 1 ! | . ca tayn» . . . [ s t ^ - n 1 n oo Figure 4.14: A screen shot of the calendar of Microsoft Outlook with a frame for adding a new appointment • £|IB I Edit go loots Action* yelp E33P Close All I * m i Save as Wfrh Page .. E D M * import arid E-iporf . F - i - co-r-p : < i Wly Calendars X .H Calendar Open a shared Cakridv 3K 4 ° ° 5 0 0 goo . Mall . , ...r-.r.U.r 7oo ' i . I CJHUaCM gOO goo 1 1 iiiiilitlitfidl in 0 " ik> to school (#99 busstop) Buy texts (bookstoie) Goto lab (ICICSI08) C5 Have lunch i«rh. ! r s i Figure 4.15: A screen shot of Microsoft Outlook calendar with some events entered 59 Import and Export Wizard CjSoose an action to perform: E<p:rUoaf l t '. .'ZZZZZ Import a VCARD file (.ver") Import an Calendar or vCalendar file (.vcs) Import from another program or file Import Internet Mail Account Settings Import Internet Mail and Addresses Description Export Outlook information to a file for use in other programs. Export to a File I Cancel Comma Sepa'ated Values (OOSJ " I Comma Separated Values (Window: : Microsoft Access i Microsoft Excel Personal Folder File (.pst) ; Tab Separated Values (DOS) •Tab Separated Values (Windows)^ .<gad ||_ jjext> j j Cancel | (a) (b) Export to 4 File • Export to a File Select feeder to export from; iSjperUserV'ly D0cuments\5chedule.txt [srjwse.. (d) Export lo a File ; The following actions wil be performed; 9\ Export 'PqattOf^fm^r. Calendar: [ M ap Custom Fields... j This ma/ take a few minutes and cannot be canceled. j <6ack |j Finish j j Cancel | Set Date Range The file format that you are exporting to does not diractly support recurring Outlook appointments or tasks. Only appointments: or taste that fal within the following date range 'will be exported, In addition, individual occurrences will be created for recurring appointments and tasks that fall within the date range. Appointments and Tasks (including individual occurrences of recurring appointments or task) that occur outside of the date range will not be exported. Export and create individual occurrences of appointments or tasks that occur between: Figure 4.16: A sequence of screen shots when exporting event information from Microsoft Outlook calendar GO •h Modify or view the map information D ; Select a campus I Select a building j Select a floor j Select a room x min; xmax; y tithe y max: upper bound: accessible: image: Save Change Close Figure 4.17: A screen shot of the frame for modifying or viewing map information jar? Modify or view the map information i . 'D |<Sj Name: SouthEast x min: Select a campus J250.0 meters) xmax: 1 1350.0 m e t e r t s i Select a building j ymni: [450.0 m e t e r ( s ) ymax: Select a floor J550.0 m e t e r ( s ) Select a room t upper bound: (200.0 m e t e r j s ) accessible: Il 1 image: i m a g e ' s j u t h E a s i J P G | Save Change Close Figure 4.18: A screen shot of the frame for viewing campus information 61 Chapter 5 Implementation We implemented our project in Java, which is a widely-used, object-oriented (00) programming language. Object orientation is an approach to organizing both the problem and its solution as a collection of objects, together with their data structure and behavior, in the software development process [23]. There are seven charac-teristics to OO representation: identity, abstraction, classification, encapsulation, inheritance, polymorphism and persistence. These characteristics assist developers in embracing some or all notions involved in object orientation. Unified Modeling Language (UML) is used in this chapter to generate a design for the Scheduler and Route Planner System, since U M L can competently illustrate the concepts of the 0 0 process. U M L can be used throughout the software development process, because 0 0 concepts apply to all stages of the process. In particular, U M L can be used to describe different design alternatives in a visual manner. 5.1 Inheritance and Polymorphism in Java Among all of the characteristics of 0 0 representation, inheritance and polymor-phism are two major ones allowing code reuse. In a large project, code-reusing is exceptionally useful. In our project, the object classes representing an architectural 62 element are hierarchical. Campus is at the highest level, while Room is at the low-est. To insert one more sub-level, we can apply the inheritance in Java using the following syntax: public class NewClass extends Room{ / / class definition here. }• Functions from the superclass or any ancestor are inherited. However, if a function behaves better in another manner, a new function declaration and definition can be implemented using the same prototype as that of the existing function. The existing function is overloaded by the new one for the new class. This is the main concept behind polymorphism. In the design process, we used a U M L diagram to describe objects, classes, behaviors and the relationships between them. The drawing can be done with paper and pencil, so we can easily modify and improve the design, or even change it entirely. Figure 5.1 shows the first cut at the design of our system. It includes nine boxes representing nine classes needed for our project. Four levels of hierarchy of architectures in our sample world are related using the "is-a" arrow. Most functions are implemented in the topmost level, Campus. Campuses consist of a Path and a CamPred, which is a class representing a set of campuses with their predecessors' names captured. Graph draws the interface items of the system. In Graph, lists of campuses, buildings, floors and rooms are maintained for the client. ConstructMap helps Graph to build the map database, while TransferSchedule builds the list of events. With the gradual development of the project, two more classes, Along and ImageList, were considered helpful. Along is the direction and name, if applicable, from a place to its neighbor. Hence, we can provide more detail to users with the Along class. ImageList reads in an X M L file and parses a list of images that are available for users to view. Therefore, the administrator can manage all images 63 Figure 5.1: First cut at the Scheduler and Route Planner System design easily. Figure 5.2 shows the final cut at the system design, which involves some possible class attributes and behaviors. 5.2 System States and Activities In addition to the static view of the system, U M L diagrams provide a dynamic view to illustrate system states and activities [23]. State and activity diagrams are two general approaches to describing a dynamic model of the system. Since most behaviors of our system are straight-forward, we will illustrate only the activity diagram for computing the shortest path from a start node to a destination node and for prompting reminder messages. In a U M L activity diagram, a filled circle represents the start node, while a black circle inside a white circle represents the end node. A. rectangle represents a state, with an arrow showing transitions from one state to another. A diamond indicates that a decision should be made, and two different steps will follow. 64 Campus heiglibors: Vector distances: -Vector-.:coord:-dpuble[] getNeighbor() slibrtestPathO Building ;Floor Room Along coord: double[] directi'o'n:-int' getDirectioriQ campuks;-,Vectbr predecessors: Vector distances:Vector addCampus.Q-rembyeMinimumO' hodesVVector totahdistance:- double estim -distance:; double; getNodef) getTptalDistance'O Graph events:. Vector' remmdTime:Vector details:' Vector bgFfameWrame leftPane: JPane rightPane:.JPane': newButtoh:JButton labet.XTextArea' couriter::Thread. getScheduleO getPaintO addButtbnList'ehersO ;TransferSchedule: textfilename: String -xmlfilenarne: String 'traiisferToXHtO ' trans ferToTextQ ;CoristructMap places:;Vector. filename:-String constr-uctPlacesQ connectNeighbors.C getPlaceO inodifyPlaceO' ImageList images-Vector Figure 5.2: Final cut at the Scheduler and Route Planner System design 65 J Verify ii destination =, start Y e s . * output N U L L Get high- le ye 1 p a ths !'Eefine..each node to one. lower 1 eve 1 till the lowest Calculate ths shortest: path No; Yes +• eliminate ths path Figure 5.3: The activity diagram for the Hierarchical Shortest Pathfinding algorithm 5.2.1 Comput ing the Shortest Pa th To compute the shortest path from a start node to a destination node, the system verifies of whether the destination node is the start node first. Figure 5.3 shows that after the verification is made, NULL is output if it is true. Otherwise, determining high-level paths and other activities will be involved. After one or more high-level paths are found, a refinement is done recursively for each high-level path until the lowest-level architectural element is reached. Some paths may be pruned away in the recursive loop if their lengths exceed a upperbound limit. The system calculates the minimum length among all the paths left. Following the arrow on the diagram, the shortest path will be found at the end node. 5.2.2 Prompt ing Reminder Messages Figure 5.4 shows the activity diagram for prompting both manual and automatic reminder messages. The manual reminder times are input by the users, so the system 66 can obtain it from the schedule.txt file. An automatic time requires some calculation after the shortest path is found for each event. The program obtains the system time and compares it with the reminder times. If times match, the system sends prompting messages to remind the user. This process executes until the program ends. Parse the text file. comainiug schedule irif• Obtain manual reminder-times Compute the shortest path Calculate the •automatic reminder time. Compare current clock time with, both remmder'tiines V,erify if..current time equals either of.reminder: times Verify i f :the program .ends N o Yes. • Yes- Prompt a reminder message-Figure 5.4: The activity diagram for the manual and automatic reminder function 67 Chapter 6 Experiments and User Study The literature on wheelchair navigation, discussed in Chapter 2, suggests devel-oping a autonomous or semi-autonomous system to physically control wheelchair movement. However, problems may arise when wheelchair users are travelling in an unknown place. The purpose of our project is to solve these problems and provide a user-friendly interface design. Situations vary depending on road conditions. In this chapter, we describe an evaluative study designed to address questions regard-ing the usability of our system, comparing the performance of wheelchair users and non-wheelchair users. 6.1 Motivating Scenarios The Scheduler and Route Planner system can be useful in different cases. Two examples are discussed as follows. The first one describes a scenario for a wheelchair user who has used the system for a while. The user can reject a path that the route planner suggests to him/her, which modifies the system's known navigating ability of the user. The second example involves a new. wheelchair user, who may need a more detailed description of the nodes on each path. 1. Mary has been using a wheelchair for ten years. She is a staff member at Disability Resource Center at UBC, and her activities mostly take 68 place in the northeast quadrant of the campus. Mary is going to attend a talk on cognitive assistive systems at the Department of Computer Science (CS) located in the southwest quadrant of the campus. She has little information about the building housing the CS department. With the Scheduler and Route Planner system, Mary inputs the destination of the event, and a path is found with detailed descriptions of which ramps and elevators she should take. Since Mary has been using the system for several months, it notices that some slopes and roads are too rough for her and eliminates paths invoking those slopes and roads. She arrives at the CS department and attends the talk. 2. John, a student at UBC, injured his leg one month ago. He usually carpools to school with his friend, but his friend is out of town today so he is going to school by bus. He plans to buy some text books, but he doesn't know the accessible wheelchair routes from the bus stop to the bookstore. Therefore, he mounts a tablet PC with our system installed on his wheelchair. The route planner provides John with the easiest path, as well as. relevant road conditions. John follows the route and arrives at the bookstore. After John buys the text books, the scheduler alerts him that he has a class in ten minutes. He checks the route planner again for the best path available to get him there on time. 6.2 Evaluators and Task Guidelines In this evaluative study, we invited two wheelchair users, Dave and Yasaman, and two non-wheelchair users, Chao and Jerry. Dave has used a wheelchair for 31.5 years and Yasaman for 4 years. Table 6.2 shows a summary of three participants' relevant information, with the same column attributes as in Table 3.1. Evaluators are asked to perform a number of actions that assume they are students in the Department of 69 Computer Science at UBC, and are working in their research lab, the Laboratory for Computational Intelligence (LCI). Each participant takes about half an hour to complete the following list of tasks. Their performances are measured in terms of correct and incorrect button clicks, total time, number of major questions, etc. Table 6.1: Summary of participant information Participants Use Wheelchair Age Gender Education Computer Use Dave Yes 50 Male 16 Often Yasaman yes 25 Female 14 Often Chao No 25 Male 18 Often Jerry No 25 Male 16 Often 1. Assume that you are at ICICS 108 (named ICCS108 in the database), and the current time is 1:10:00 P M . You will have a class at Dempster 101 (named D E M P 101 in the database) in twenty minutes. 2. Create a new event using the scheduler function from our system. Enter the information of the next class, as follows: • Subject: Go to class .• Start Date: 09/17/2006 • Start Time: 1:30:00 P M • From: ICCS108 • Location: DEMP101 • Reminder: 1:20:00 P M 3. Wait until a reminder is triggered. Then view the high-level path, which provides you with a general idea about the path. 4. If you are not sure of the exact path from LCI to the classroom, view the detailed path by clicking the "Detail" button. You can also click on the room button to view its information. 70 5. Following the path, you arrive at a steep ramp that you may not be able to pass through. Reject the current location. 6. Change your position to the previous step by double-clicking on the corre-sponding button. (Note: this will let the system know where you are along the path.) 7. Edit the event information (i.e., remove the text in the "From" field). 8. Review the high-level and low-level paths. Follow the direction and arrive at DEMP101. One research investigator accompanied with each evaluator during the test. The researcher provided an introduction to the system and help when the evalua-tor needed. The researcher also observed the performances of all evaluators, and compared the performance differences between wheelchair users and non-wheelchair users by the required time, the number of false button clicks, the number and con-tents of questions, etc. In addition, interest in system appeared by each evaluator was also observed by the researcher during the discussion of evaluating the system and suggestion or recommendation of future work. 6.3 E v a l u a t i o n R e s u l t s a n d R e c o m m e n d a t i o n We conducted survey with each evaluator regarding the usability of the Scheduler and Route Planner system. A l l evaluators were interested in the system design, but suggested the following modifications and improvements: • Although an alternative path is provided to users after they reject a difficult location, alternative paths could be provided in general as different options for the users. • The application covers only a small local area, and a major portion of the paths are indoors. Therefore, transportation is not shown. However, transportation 71 methods may affect the pathfinding and user speed. Hence, an indicator of transportation methods is suggested. • Information about whether a door is powered, as well as the door width, could be added to the database. • The North direction should be included on each picture. • More directional arrows are needed when an edge is long or involves turns. After the user study test, the participants were asked to evaluate four aspects of the system: interface design, functionality satisfaction, ease of use, and willingness for future use, as explained in Chapter 3. Figure 6.1 displays the results. 100 90 80 70 4-60 50 40 30 + 20 10 0 System Evaluation • P1 • P2 • P3 • P4 Interface Functionality Ease of Use Willingness for Satisfaction Satisfaction Future Use Figure 6.1: Evaluation of the Scheduler and Route Planner system 72 Chapter 7 Conclusions and Future Work The major goal of this research was to discover how modern technologies might help wheelchair users carry out daily activities. A significant sub-goal was to build an efficient system that helps wheelchair users in route-finding. We met this sub-goal through the design and creation of the prototyped system and the evaluative studies. In this chapter, we summarize how this sub-goal was accomplished and why it is an significant step towards the major goal. Finally, we discuss anticipated future work. 7.1 Conclusion In this thesis, we have focused our efforts on building the Scheduler and Route Planner system, which can find both an abstracted high-level path and a detailed' low-level path. There has been extensive research in the cognitive science field that aims to provide help in daily activities. Some researchers devoted -their effort on building systems to provide pathfinding and direction-reminding help for people with mental problems or for forgetful clients. However, these systems provide only limited information about direction, but not related to accessible routes. Hence, these systems are not applicable for wheelchair users. Although systems exist to help wheelchair users avoid obstacles, these system do not provide future path-planning. In the system design and evaluation phases, we used interactive methods, 73 which involved potential users, both wheelchair and non-wheelchair individuals, to evaluate each level of system prototypes and the actual system. This evaluative study lets us realize user's need towards interface specification and functionality requirements. We apply efficient path-finding algorithms, the HSP and HSPA*, in our system, which runs on hierarchically presented maps containing only wheelchair accessible routes. In addition, the scheduler provides basic functions for users to keep track of their activities. With Microsoft Outlook providing augmentative functionality, our system worked well during the evaluative tests. Manual and automatic remain-ders are applied to announce events in the near future. An automatic reminder is created by the system after it calculates the approximate distance to the next destination. The system takes user speeds as inputs and estimates accurate auto-reminder time. The system is also designed to learn user abilities, which are assumed to be strong originally and are updated along the execution process. An alternative path is displayed, instead of a shorter path that contains non-acceptable locations for individual user. With all of the functionality stated above, the new cognitive assistive system is able to contribute to helping users. The evaluative user study presented in Chapter 6 shows that the evaluators appeared interested in using the system in the future. Nevertheless, we can extend our research in several areas, which will be discussed in the next section. 7.2 Discussion and Future Work There are six areas where further research is required. First, the HSP and HSPA* algorithms can be refined and tested further to achieve even better performance. Second, the Java GUI interface implementation needs improvement. Third, the database could be enlarged to include more buildings and information. Fourth, advanced machine learning methods could be applied to refine the user model. Fifth, 74 the system could be redesigned for other classes of users. Finally, we could enhance our system to make it more marketable. 7.2.1 O p t i m i z i n g the H S P and H S P A * A l g o r i t h m s In this thesis, the Hierarchical Shortest Pathfinding (HSP) algorithm is presented and its running time analyzed. The speedup from eliminating unnecessary high-level paths is remarkable, and good performances of HSP is expected in real-world pathfinding problems. There are a few directions that this research could be ex-tended beyond the work shown in Section 4.2. First, more examples are anticipated to analyze the performances of the four algorithms. We analyzed the running times of the HSP and HSPA* algorithms based on artificial examples. We shall apply the algorithms on more actual examples, such as the whole U B C campus. Second, as described in Section 4.2.3, the HSPA* algorithm contains two parameters, namely a and f3. The best-fit values of these two parameters may vary depending on differ-ent problems. Therefore, it may be worthwhile learning well-suited values for these two parameters using stochastic local search methods. Third, comparison of HSP, HSPA* and other hierarchical pathfinding algorithms could be further investigated in terms of their running times and performance. 7.2.2 I m p r o v i n g Inter face Imp lemen ta t i on The locations of buttons representing path nodes may not be ideal. These buttons show the step numbers in the found path, since the button size is not large enough to contain a full name of a place. They should show the names of their corresponding places, so that the users do not need to check the path description to see what position they are in. A better layout method could be used to solve these problem in the future. In addition, as pointed out by the evaluators, there is only one arrow from one node to the next node, which may not be clear if the road is long and involves turns. In other words, we should enlarge the database to include the 75 information of the arrows and refine the Along.Java class to accept multiple arrow positions. 7.2.3 Improving Database Management Our current database includes only a few buildings on U B C campus, so route-finding only functions in this limited area. We could build maps containing the whole U B C campus, or the Vancouver city, or even larger areas. We could also include information about whether a door is powered, its width, etc. A l l the data could be located in a shared online database, so that users can exchange information by uploading and downloading files. Networking techniques and skills would be needed, and extra care would be required to analyze which data file has the latest update and to combine parallel changes. 7.2.4 App ly ing Advanced Machine Learning Method In Section 4.3, we mentioned that more sophisticated machine learning methods are anticipated for modelling the specific user model. This refinement would allow the system to better understand the users, and provide more suitable paths for them. The new user model could include the transportation methods, so the system could provide better estimated travelling time. 7.2.5 Redesigning for Different Classes of Users The system can be extended or redesigned for different classes of users. With voice communication, our system is also suitable for the visually impaired. Also, the hierarchical pathfinding algorithm can be redesigned to generate procedures of per-forming activities. The procedures can be viewed as general or detailed steps, which would be helpful for people with mental problems, such as Alzheimer's disease. In addition to people with impairments, normal people can get help from our system if it is installed on a handy device and applied on a large map. 76 7.2.6 F u t u r e Deve lopmen t i n the M a r k e t Beyond the work in this research, our system can be extended to advanced soft-ware which can be installed on a smaller device, such as P D A and cellular phone. Since cellular phone is easy to carry and widely used, our system, with different implementation and interface design that suit for cellular phone, could be more marketable. 77 Bibliography [1] Clever project, http://13d.cs.colorado.edu/clever/. [2] Overwhelming need for wheelchairs, http://wheelchairfoundation.ca/. [3] Aware home project, http://www.cc.gatech.edu/fce/ahri/. [4] Nursebot project, http://www.cs.cmu.edu/~nursebot/. 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Partial pathfinding using map abstraction and refinement. In AAAI-05, pages 1392-1397, 2005. [29] H. Wakaumi, K . Nakamura, and T. Matsumura. Development of an automated wheelchair guided by a magnetic ferrite marker lane. In Journal of Rehabilita-tion Research and Development, volume 29, pages 27-34, 1992. [30] H. Yanco. Wheelesley: A robotic wheelchair system: Indoor navigation and user interface wheelchair. In New York: Springer, pages 256-268, 1998. in Assistive Technology and Artificial intelligence, V . Mittal, H . Yanco, J. Aronis, and R. Simpson, Eds. 80 Appendix A Consent Form for All Evaluators Participants were asked to sign a consent form prior to participating in the evaluation phase. The next page is the standard consent form used in this study. We paid the evaluator $10 as an honorarium. 81 UBC THE UNIVERSITY OF BRITISH COLUMBIA Department of Computer Science 2366 Main Mal l Vancouver. B . C . . V 6 T lz4 July 31 2006 Consent Form A scheduler and Route Planner for Wheelchair Users Principal Investigator Alan Mackworth. Ph .D. Student Investigator Suling Yang i Project Purpose and Procedures This project is designed to develop a system of a scheduler and route planner installed on a small device, such as a tablet P C . for people using wheelchairs. This project also forms the basis of the student investigator's thesis. The purpose of conducting a user study is to improve the system interface design. Y o u will be asked questions about interface prototypes. This session will take no longer than ten minutes. Your answers to the questions asked by the investigator and your suggestions may be used for a research presentation in the Department of Computer Science at the University of British Columbia. Confidentiality and Contact Information The identities of all participants will remain anonymous and will be kept confidential. We are very grateful for your participation. If you have any questions or require further information about this project, you may contact either of the investigators. Consent Your participation is entirely voluntary and you may refuse to participate or withdraw from the study at any time. Your signature below indicates that you have received a copy of this consent form for your own records, and that you consent to participate in this project. You do not waive any legal right by signing this form. I; - agree to participate in this project. M y participation is voluntary and I understand mat I can withdraw at any time. Participant's Signature Date Student Investigator's Signature Date 82 Appendix B Questionnaire for All Evaluators B.l Questionnaire for the Evaluators Who Participated in the Design Phase Participants in the evaluative study for design specifications were asked questions regarding their opinions and suggestions about our proposed system. Text page shows the questionnaire for the evaluators who participated in the design phase. B.2 Questionnaire for the Evaluators Who Participated in the System Evaluation Phase Participants involved in the system evaluation phase will be asked the same questions as were the design evaluators. Participants for this phase will also be asked the following questions: • Did you encounter any problems in using the device that has the Scheduler and Route Planner system installed on it? If yes, please explain. • What functions are not too clearly shown but you think are useful? How would you change the design of these functions? 83 What functions are worth adding to the system? What level of usage of these functions would you expect? Did you need the investigator's explanation or any help with the test? If yes, what problem(s) or question(s) did you encounter? 84 UBC T H E UNIVERSITY O F BRIT ISH C O L U M B I A : Department of Computer Science 2366 Main Mall VaucouveivB.G.. V6T lz4 July 31. 2006 Questionnaire A Scheduler and Route Planner for Wheelchair Users 1. Personal Information: • Age Yea'r(s) of-using wheelchair Year(s) of education Frequency:of computer use:;a.-Often b. Sometimes c.Seldom d..Never 2. Are you satisfied with the locations of intefface:coinpotients?;if not. what.makes you-unsatisfied? 3. Are;. you: satisfied with the. number of'clicks to access a certain function? If not. what makes vou unsatisfied? 4. Can you give, a erade out of 100% for thejnterfacedesian? 5. Are .you satisfied with the functionality' thafthe system provides? If not,.what makes you .unsatisfied? Can,you give; a.grade :out of 10096 for-this aspect? 6. Do.you.think-.th.e-. system is.ease tojuse? What aspects.should be improved? Canyou. sive a-erade but of 100% for the ease of use of our system? 7. Would you use our system if it is'produced in the'future? Can you givea grade'out of 100% for your willingness, to do so? 85 

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