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Overground vs. treadmill-based robotic gait training to improve seated balance in people with motor-complete… Chisholm, Amanda E; Alamro, Raed A; Williams, Alison M M; Lam, Tania Apr 11, 2017

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RESEARCH Open AccessOverground vs. treadmill-based robotic gaittraining to improve seated balance inpeople with motor-complete spinal cordinjury: a case reportAmanda E. Chisholm1,2*, Raed A. Alamro1,2, Alison M. M. Williams1,2 and Tania Lam1,2AbstractBackground: Robotic overground gait training devices, such as the Ekso, require users to actively participate intriggering steps through weight-shifting movements. It remains unknown how much the trunk muscles areactivated during these movements, and if it is possible to transfer training effects to seated balance control. Thisstudy was conducted to compare the activity of postural control muscles of the trunk during overground (Ekso) vs.treadmill-based (Lokomat) robotic gait training, and evaluate changes in seated balance control in people withhigh-thoracic motor-complete spinal cord injury (SCI).Methods: Three individuals with motor-complete SCI from C7-T4, assumed to have no voluntary motor functionbelow the chest, underwent robotic gait training. The participants were randomly assigned to Ekso-Lokomat-Eksoor Lokomat-Ekso-Lokomat for 10 sessions within each intervention phase for a total of 30 sessions. We evaluatedstatic and dynamic balance control through analysis of center of pressure (COP) movements after each interventionphase. Surface electromyography was used to compare activity of the abdominal and erector spinae musclesduring Ekso and Lokomat walking.Results: We observed improved postural stability after training with Ekso compared to Lokomat during staticbalance tasks, indicated by reduced COP root mean square distance and ellipse area. In addition, Ekso trainingincreased total distance of COP movements during a dynamic balance task. The trunk muscles showed increasedactivation during Ekso overground walking compared to Lokomat walking.Conclusions: Our findings suggest that the Ekso actively recruits trunk muscles through postural control mechanisms,which may lead to improved balance during sitting. Developing effective training strategies to reactivate the trunkmuscles is important to facilitate independence during seated balance activity in people with SCI.Keywords: Balance, Gait, Motor activity, Robotics, Spinal cord injuryBackgroundRehabilitation for individuals with a spinal cord injury(SCI) mainly focuses on achieving functional independ-ence in self-care and mobility [1]. The ability to maintainpostural stability during sitting is important to performdaily functional activities [2]. However, the loss ofnormal postural synergies along with weakness or par-alysis of the trunk muscles significantly impairs sittingbalance [3]. Thus, improving seated postural stability isan important goal in most SCI rehabilitation programs.For people diagnosed with motor-complete SCI aboveT6, it may be incorrectly assumed that they are unableto engage muscles of the trunk since established clinicalmethods (i.e. International Standards for NeurologicalClassification of Spinal Cord Injury, ISNCSCI) rely onsensory function in the thoracic spinal segments to de-termine the level and completeness of injury. Evidence* Correspondence: achisholm@icord.org1School of Kinesiology, University of British Columbia, Vancouver, BC, Canada2International Collaboration On Repair Discoveries, Vancouver Costal HealthResearch Institute, University of British Columbia, 818 West 10th Avenue,Vancouver, BC, CanadaV5Z 1M9© The Author(s). 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, andreproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link tothe Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.Chisholm et al. Journal of NeuroEngineering and Rehabilitation  (2017) 14:27 DOI 10.1186/s12984-017-0236-zfrom previous studies show that activity in the trunkmuscles can be detected by manual palpation [4, 5], andelectromyography (EMG) [6]. These methods have dem-onstrated the presence of abdominal muscle activity belowthe level of injury in individuals with motor-complete SCI[6, 7]. As well, the use of transcranial magnetic stimulation(TMS) has revealed motor-evoked potentials in the ab-dominal muscles below the level of injury for people clas-sified as motor-complete SCI based on the ISNCSCIexamination [7]. This implies some preservation of thecorticospinal tract to the trunk muscles, raising the possi-bility for rehabilitation interventions to improve posturalcontrol and function in this sub-population.Dynamic postural control is a well-known requirementfor successful gait performance [8]. Robotic lower limbdevices, such as the Lokomat, facilitate gait rehabilita-tion based on principles of body-weight support (BWS)treadmill training. However, it remains unknown howthey engage postural muscles to control balance duringstanding and walking. Research in people with multiplesclerosis has demonstrated that gait training strategiesthat provide BWS may reduce the demand on posturalmuscles [9]. Gait training with the trunk passively sup-ported by a harness implies less need for active dynamicstabilization. Consequently, the normal trunk muscle ac-tivity and movements that are important for the reten-tion of posture and balance may not be involved in thegait training.Advances in robotic technology, such as the Ekso™, al-lows for overground training and may provide a betteropportunity for individuals with motor-complete SCI toengage the trunk muscles. The Ekso allows users to ac-tively participate in the control of walking through sub-tle trunk motions to shift their weight over theappropriate foot in order to trigger each step. While thisdevice is new to rehabilitation centers, current studieson a similar device have demonstrated safety and effi-cacy of use for gait training [10, 11].It has also been demonstrated that training in one task(e.g. gait training) may transfer beneficial effects to othertasks [12, 13]. If training with robotics challenges dy-namic postural control, secondary benefits of trainingmay include the transfer of improved postural control toseated balance activities. Impaired motor and sensoryfunctions after SCI can contribute to difficulty sittingunsupported, and compensatory patterns of muscle acti-vation are often engaged to maintain postural support[3]. This is even more critical for individuals who are re-quired to perform activities of daily living from a wheel-chair, and need to be able to reach in all directions.The purpose of this case study was to determine howpostural control muscles of the trunk are challengedduring different methods of robotic-assisted gaitperformance, and evaluate changes in seated balancecontrol after gait training with robotics in people withmotor-complete SCI above T6. If overground robotic-assisted gait training (i.e. Ekso) is a successful interventionto engage the trunk muscles compared to a treadmill-based method (i.e. Lokomat), we propose that increasedlimits of stability (LOS) during dynamic balance controland reduced COP sway during static balance control willbe observed.MethodsThree participants who sustained a traumatic motor-complete SCI between C7-T4 (American Spinal InjuryAssociation Impairment Scale; AIS A and B) 18–25years ago (Table 1) volunteered for this study. All partic-ipants used a wheelchair for mobility and were inde-pendent in their activities of daily living. They had nosignificant medical history, and one participant was tak-ing prescription medications for a bladder infection. Par-ticipants were able to maintain an unsupported seatedposture for at least 1 min. They were free of using ro-botic gait devices (i.e. Lokomat and Ekso) for 1 yearprior to the study. All participants provided written in-formed consent and all procedures were approved bythe University of British Columbia Clinical ResearchEthics Board.Gait training interventionWe used an alternating treatment design with threeintervention phases to compare the Ekso and Lokomatmethods of robotic gait training. The two groups wereEkso-Lokomat-Ekso and Lokomat-Ekso-Lokomat, with10 training sessions in each intervention phase for atotal of 30 sessions and no washout between interven-tion phases. Randomized allocation of the participants tothe groups was concealed at the examination. Seatedbalance control outcome measures were repeated at theend of each intervention phase.Table 1 Participant demographic and clinical dataP1 P2 P3Age (y) 41 42 39Weight (kg) 92.3 68.0 68.8Height (cm) 183 170 178Gender M M FInjury Level T3 C7 T4AIS A B APost Injury (y) 23 18 25UEMS (/50) 50 34 50Pin Prick (/112) 40 62 43Light Touch (/112) 41 67 43AIS American Spinal Injury Association Impairment Scale, UEMS upperextremity motor score. Higher scores on pin prick and light touch scales of theISNCSCI indicate better sensory functionChisholm et al. Journal of NeuroEngineering and Rehabilitation  (2017) 14:27 Page 2 of 8Participants performed up to 45 min of robotic-assistedgait training 3–4 times per week. Rest breaks were pro-vided when needed by the participant. Participants re-ported their rating of perceived exertion (RPE) on theBorg CR-10 Scale every 10 min during training [14].Initial training with the Ekso™ (Ekso Bionics, Califor-nia, USA) consisted of sit-to-stand, standing balance,weight shifting, and stand-to-sit functions. Training fo-cused on improving walking performance (triggeringsteps, executing a turn, and stopping). All participantswere in ‘ProStep’ mode, wherein steps are automaticallytriggered when the weight-shifting targets are achieved,within the first session. Auditory cues for the weight-shifting targets were provided as feedback, and removedwhen an efficient gait pattern was maintained. The 10-meter walk test (10MWT) was used to record their fast-est possible gait speed during training, which will reflecttheir ability to weight shift and trigger steps efficiently.The best time of three trials was recorded. For eachsession, we documented ‘Up Time’ (combined standingand walking time), ‘Walk Time’ (walking time only), andthe number of steps taken.Gait training with the Lokomat robotic gait orthosisfocused on increasing gait speed. Treadmill speed wasset to the fastest speed that the patient could tolerate,and subsequently increased by increments of 0.1 km/hevery 10 min. If spasticity was exaggerated or poor footcontact with the treadmill was observed, the speed waslowered by 0.1 km/h. The level of BWS was adjusted tothe minimum tolerated by the patient while ensuring ap-propriate stance phase kinematics. For each session, wedocumented BWS, walking speed and total distance.Seated balance controlTwo baseline assessments of seated balance control wereconducted prior to training and separated by 1 week.The assessment was repeated within 1 week of the finaltraining session at the end of each intervention phase.We evaluated seated balance control by asking partici-pants to sit on a forceplate (Bertec; Columbus, Ohio)covered with a foam pad. The forceplate was elevated sothat the feet were off the ground. Participants wereinstructed to sit as still as possible while performing twostatic sitting balance tasks (quiet sitting with eyes openand with eyes closed) for 60 s with their arms crossed atthe chest (Fig. 1a). During the eyes open task, individualsfocused their gaze on a target 10 ft away. This protocolhas been previously used to verify impaired seated pos-tural control in people with SCI [15]. We also performeda dynamic sitting task to evaluate their LOS in the eightcardinal directions. During the LOS test, each trialstarted with 20 s of quiet sitting with eyes open to estab-lish a baseline limit, calculated as the COP mean pos-ition plus four times the standard deviation (SD). Then,we provided visual biofeedback of their COP position (e.g.green dot) and baseline limits (e.g. a red box scaled to thebaseline limit and centered at the mean COP pos-ition) on a computer monitor. Participants wereinstructed to lean as far as they could without losingtheir balance and return their COP within the base-line limit (Fig. 1b). There was no time constraint tocomplete the movement and the order of each direc-tion was randomized. The movement direction waspresented as a yellow arrow on the monitor. Two tri-als were recorded for each static balance task and theLOS test. This assessment was repeated 1 week laterto establish a stable baseline. Data were collected at100 Hz and stored for offline analysis.Fig. 1 A picture of the seated balance control measurement setup;a participant is seated on the forceplate with feet off the ground,and b the computer monitor displays the limits of stability test (COPposition – green dot, baseline limit – red box, movement direction –yellow arrow)Chisholm et al. Journal of NeuroEngineering and Rehabilitation  (2017) 14:27 Page 3 of 8These data were subsequently used to calculate theroot mean square distance (RDIST) and velocity (RVEL)from the mean COP position to examine overall posturalstability and the amount of postural activity during thestatic balance tasks [16]. We also calculated a 95% confi-dence ellipse area (AREA-CE) during static balance tomeasure stability performance [16]. The LOS test wasscored as the total distance (sum of the maximum dis-tance between the COP mean position and the furthestpoint in each direction). A mean was calculated per con-dition for all outcome measures.Participants also performed two clinical measures ofseated balance function at baseline and after each inter-vention phase. The T-shirt Test measures the time takenby the participants to don and then doff a T-shirt. Themean of two trials was calculated with the total time.For the Modified Functional Reach Test (mFRT), partici-pants sat unsupported with their hips, knees, and anklesat 90°, then pushed a ruler forward with both arms andheld their maximum position for at least 2 s. The meanof three trials was calculated with the distance. Thesetests of unsupported sitting have been proven reliableand valid in SCI [17, 18].Gait assessmentWhen participants were able to walk overground withthe Ekso in ‘ProStep’ mode at a gait speed of at least1.0 km/h, which is the minimum speed of the Lokomat,we conducted an assessment of trunk muscle activitycomparing each method of robotic-assisted gait. EMGdata were recorded using surface electrodes (SX230-1000, Biometrics Ltd., Newport, UK) connected to aportable data acquisition system (DataLOG, BiometricsLtd., Newport, UK). Electrodes were placed on the rightside for the rectus abdominis (RA)–3 cm lateral and2 cm caudal to umbilicus; external oblique (EO)–2 cmbelow the lowest point of the rib cage [6]; and erectorspinae (ES)–2 cm lateral to the T3, T12 and L4 spinousprocesses [19]. EMG signals were recorded at 1000 Hz.Foot switches were used to determine heel strike andtoe off for each step. Participants performed orattempted a maximum voluntary contraction (MVC) atthe trunk for flexion, lateral flexion, rotation and exten-sion. Baseline EMG activity for all muscles was recordedwhile participants were lying supine.Participants performed two walking conditions atmatched speeds: 1) Ekso and 2) Lokomat. Three trialswere recorded per condition. Overground speed was cal-culated from the time taken to traverse the middle 10 mof the 12 m path. During Lokomat walking, we used astandardized BWS level set at 50% of the participant’sbody weight.The average EMG amplitude recorded during MVCwas used for normalization. EMG data was filtered witha sixth-order dual pass Butterworth filter at a high-passof 30 Hz to remove electrocardiography artifact, thenrectified and filtered with a sixth order dual pass Butter-worth filter at a low pass of 50 Hz using custom routineswritten in MATLAB (Mathworks, Natick, MA, USA).Data were separated into steps synchronized to rightheel strike. The average time series amplitude of at least20 steps was calculated per condition. Then, the rootmean square (RMS) of the EMG signal over each gaitcycle for each muscle was calculated to quantify theEMG amplitude of the trunk muscle activity. RMS wasalso calculated for the baseline EMG activity for all mus-cles during static supine position.ResultsP1 and P3 were randomized to Ekso-Lokomat-Ekso,while P2 trained in the Lokomat-Ekso-Lokomat group.All participants were able to physically tolerate robotic-assisted gait training; all sessions were completed and noadverse events occurred. RPEs reported ranged from 2to 8 for Ekso training and 0.5–6 for Lokomat training.Figure 2 shows the progression of gait speed over eachphase of training.Static balance controlBaseline postural sway measures indicate that partici-pants had greater instability during eyes closed com-pared to eyes open (Fig. 3a & b). During the firstbaseline assessment, static seated balance control withFig. 2 Gait speed is plotted for each training session per participant(P1 – solid black line, P2 – solid grey line, P3 – dotted black line). Gaitspeed was determined by the 10MWT for Ekso training, and themaximum speed achieved during Lokomat trainingChisholm et al. Journal of NeuroEngineering and Rehabilitation  (2017) 14:27 Page 4 of 8eyes closed was difficult for P1, who lost his balance andhad to grab a handrail for recovery. Data on this trialwas analyzed prior to the handrail recovery. All othertrials were completed successfully.After the first intervention phase of Ekso training, P1and P3 reduced the RDIST, RVEL, and AREA-CE of theCOP during both EO (Fig. 3a) and EC (Fig. 3b) condi-tions, indicating improvement in quiet sitting perform-ance. After Lokomat training, there was a generalincrease in RDIST and AREA-CE, indicating worseningof performance. This was followed by general improve-ment in these measures (decreased scores) after the finalEkso training phase, except for the EC condition withP3. P1 continued to reduce RVEL across the Lokomatand final phase of Ekso training, while it increased backto baseline values for P3. The RDIST and AREA-CEremained similar for P2 between both baseline assess-ments and after the first intervention phase of Lokomattraining in both EO and EC conditions. P2 reducedRDIST and AREA-CE after Ekso training compared tobaseline and Lokomat training, with a larger effect in theeyes closed condition. RVEL showed no change acrossassessments for P2 with EO, but an increasing trend forEC with the exception of a slight decrease after Eksotraining.Dynamic balance controlThe change in LOS total distance showed a similar trendfor P1 and P3: increase after Ekso training compared tobaseline (indicating improved performance), then de-crease after Lokomat training, and subsequent increaseafter the final Ekso training (Fig. 3c). P2 showed nochange between baseline 2 and Lokomat training forLOS total distance, followed by an increase after Eksotraining, and then a decrease after the second Lokomattraining phase (Fig. 3c).Clinical measures of seated balance controlAll participants slightly reduced their total time duringthe T-Shirt Test and increased their distance during themFRT after Ekso training (Table 2). There was a ten-dency for greater time taken on the T-Shirt Test andshorter distance during the mFRT after Lokomat trainingas compared to Ekso training (Table 2).Trunk EMG during robotic-assisted gaitThe RA and EO muscles showed tonic activity over thegait cycle during Ekso and Lokomat walking, while theES muscles showed a burst of activity at the transitionfrom stance to swing that is more noticeable in the Eksocondition (Fig. 4a). Ekso walking produced higher trunkmuscle activity compared to Lokomat walking in all par-ticipants (Fig. 4b). In fact, mean EMG amplitude in allmuscles during Lokomat walking was similar to that re-corded during baseline.DiscussionAdvances in robotic-gait technology provide an excitingopportunity to explore possible training benefits to indi-viduals with motor-complete SCI who are otherwiseFig. 3 COP outcome measures are plotted for baseline 1, baseline 2,and post each intervention phase; mean COP RDIST, RVEL andAREA-CE of the static balance tasks (a eyes open and b eyes closed),and c mean total distance of the dynamic balance taskChisholm et al. Journal of NeuroEngineering and Rehabilitation  (2017) 14:27 Page 5 of 8unable to practice standing and walking independently.Prior to training, we observed postural instability duringseated balance that is consistent with previous reports inSCI [15, 20]. This case report demonstrates how over-ground robotic gait training with the Ekso engages thetrunk muscles and could elicit training effects on staticand dynamic seated balance control in people with high-thoracic motor-complete SCI. Conversely, after trainingwith the Lokomat, a robotic-gait system that limits trunkmovement, participants demonstrated no change in seatedbalance control. This is supported by our observationsthat RMS amplitudes of trunk muscle activity duringLokomat walking did not differ much from that recordedwhile lying supine. These results indicate that overgroundrobotic gait training presents a unique strategy to re-activate muscles of the trunk to enhance postural stability,which is important for performing functional activities ina seated posture after SCI.An important finding demonstrated by our study partic-ipants was the recruitment of trunk muscle activationwhile walking in the Ekso, even though they were classi-fied as motor-complete above T6. Previous studies haveimplemented neurophysiological assessments to showsparing of the corticospinal tract below the level of injuryin people classified as motor-complete [6, 7]. Although,we did not specifically evaluate preservation, participantsdid show greater activity during the MVCs compared toresting in a supine position. In addition, the Ekso engagedthe trunk muscles considerably above resting levels andthis may have produced a training effect.We propose that the Ekso engages the trunk musclesthrough weight-shifting movements that are required toposition the body over the appropriate foot to initiate astep. In able-bodied individuals, abdominal and back mus-cles are activated during walking and contribute to main-taining postural stability by producing angular accelerationsat the trunk in the frontal and sagittal planes [21, 22]. Dur-ing Ekso walking, we observed similar activation patterns ofthe RA, EO and ES as reported in able-bodied subjectswho walked overground at a similar speed [23]. Increasedactivity in the EO may represent greater demands for lateralstabilization during trunk shifts. However, in the Lokomat,muscle activity levels were similar to those during staticposture. It appears that the BWS may decrease or eliminatethe need to produce angular accelerations for postural sta-bility. A previous study reported reduced postural controldemands as a result of limited trunk acceleration duringgait with BWS in able-bodied adults [24]. Moreover, thepercentage of the BWS provided by the Lokomat may affecttrunk muscle activity. Trunk muscle activity recorded fromable-bodied subjects and individuals with multiple sclerosiswhile walking on a treadmill supported by a harnessshowed increased activity of EO and decreased activity ofES as BWS percentage increased [9].Table 2 Summary of clinical measures of seated balancecontrolT-Shirt Test mFRTB1 Ekso Loko Ekso B1 Ekso Loko EksoP1 10.9 9.5 10.9 9.0 5.2 5.7 4.3 5.7P3 15.1 10.0 10.8 10.2 4.2 9.3 6.7 13.0B1 Loko Ekso Loko B1 Loko Ekso LokoP2 17.9 14.7 12.9 13.4 3.3 6.3 9.0 6.0B1 first baseline assessment, mFRT Modified Functional Reach TestFig. 4 a the normalized muscle activity patterns of the rectusabdominis (RA), external oblique (EO), and erector spinae (ES)muscles are plotted over the gait cycle for P1 for all conditions;Ekso (EKSO –black line), and Lokomat (LOKO – grey line). The baselineactivity (BAS – light grey shaded area) recorded during quiet lying isalso displayed. b the average RMS amplitude across participants isplotted as a bar for each condition. Individual data from eachparticipant is also displayed (P1 – circle, P2 – square, P3 – star)Chisholm et al. Journal of NeuroEngineering and Rehabilitation  (2017) 14:27 Page 6 of 8We observed evidence of improved seated balancecontrol during static and dynamic tasks after trainingwith an overground robotic exoskeleton. The transfer ofimproved balance control from standing to sitting isconsistent with other studies showing transfer betweengait and balance functions [12, 13]. Although we did nottest this directly, it is possible that participants learnedto use sensory cues from the body during gait trainingto improve balance while sitting. By providing spatialauditory biofeedback for the lateral and forward weight-shifting movements while using Ekso, the participants mayhave become more aware of their body’s position in spacethrough improved sensorimotor integration [25, 26].Furthermore, as training is progressed, the auditory cuesare removed as long as the participant can maintain an effi-cient gait pattern. The reduced reliance of the auditoryfeedback along with the findings of improved seated bal-ance following the Ekso phases are supportive of a learningeffect, although further study is required to confirm thishypothesis.In addition, postural control training in a standingposture may offer secondary benefits aimed at overcom-ing other health problems, such as bladder infections[27], spasticity [28], blood pressure homeostasis [29],and bone demineralization [30]. Thus practicing in astanding posture may decrease the risk of secondarycomplications and subsequently improve the quality oflife of individuals with SCI. Since seated balance plays animportant role in performance of daily activities [31], de-veloping effective training strategies to enhance posturalcontrol and facilitate prevention of secondary complica-tions from prolonged sitting is important to health andquality of life for people with SCI.This case report provides important information com-paring the possible effects of different robotic-gait trainingmethods; however there are several limitations that needto be addressed. We did not provide a wash-out periodbetween interventions to eliminate the possibility of carry-over effects, and determine retention of the changes inseated balance control. For example, the positive trainingeffects from using the Ekso may have been maintainedinto the subsequent Lokomat intervention phase. We useda double baseline prior to training to determine stabilityin the COP outcome measures, however P1 showed betterbalance control at the second baseline as he had to use ahand rail to prevent falling during eyes closed during thefirst assessment. Also, it is possible that wearing a harnessand the suspension system has an influence on muscle ac-tivity during gait due to a change in trunk posture [24].The BWS system used in this study has four suspensionpoints, which may further reduce postural stability de-mands during gait training compared to other systemsthat only have one or two suspension points. Hence, theresults of this study cannot be extrapolated to othersuspension systems. In addition, our EMG assessmentprovided only a cross-sectional view of enhance trunkmuscle activity during Ekso compared to Lokomat walk-ing. Further study with a larger sample is required toevaluate the training effect of Ekso walking on trunkmuscle activity, and to confirm the clinical significance ofthe changes in seated balance control.ConclusionsIn summary, this case report has shown that over-ground robotic gait training in the Ekso enhances trunkmuscle activity relative to Lokomat, which may lead toimprove seated postural control in motor-completeSCI. These findings raise interesting possibilities forgait rehabilitation strategies for people with high-thoracic motor-complete SCI. These results emphasizethe importance of trunk control in sitting balance, andindicate the importance of recovering trunk function inrehabilitation of individuals with SCI as an approach toimprove their sitting balance required for functionalactivities.Abbreviations10MWT: 10-meter walk test; AIS: American Spinal Injury AssociationImpairment Scale; AREA-CE: Confidence ellipse area; BWS: Body-weightsupport; COP: Centre of pressure; EMG: Electromyography; EO: Externaloblique; ES: Erector spinae; ISNCSCI: International Standards for NeurologicalClassification of Spinal Cord Injury; LOS: Limits of stability; mFRT: ModifiedFunctional Reach Test; MVC: Maximum voluntary contraction; RA: Rectusabdominis; RDIST: Root mean square distance; RMS: Root mean square;RPE: Rating of perceived exertion; RVEL: Root mean square velocity;SCI: Spinal cord injuryAcknowledgementsThe authors would like to thank our subjects for participating. We would alsolike to thank Catherine Chan for assistance with the robotic gait training.FundingAEC is support by a Michael Smith Foundation for Health ResearchPostdoctoral Fellowship. TL is supported by a Canadian Institutes for HealthResearch New Investigator Award.Availability of data and materialsPlease contact author for data requests.Authors’ contributionsAEC designed the study, wrote the proposal and ethics documents, providedproject management, participated in data collection and analysis,contributed to gait training, and drafted and revised the manuscript. AMMWcarried out data collection, participated in gait training, and revised themanuscript. RAA carried out data collection and analysis, participated in gaittraining, and revised the manuscript. TL conceived of the study, participatedin its design, provided fund procurement and institutional liaisons, providedfacilities and equipment, and revised the manuscript. All authors read andapproved the final manuscript.Competing interestsThe authors declare that they have no competing interests.Consent for publicationAll participants provided written informed consent to publish the data.Ethics approval and consent to participateAll participants provided written informed consent and all procedures wereapproved by the (Blinded) Clinical Research Ethics Board.Chisholm et al. Journal of NeuroEngineering and Rehabilitation  (2017) 14:27 Page 7 of 8Publisher’s NoteSpringer Nature remains neutral with regard to jurisdictional claims inpublished maps and institutional affiliations.Received: 25 November 2016 Accepted: 22 March 2017References1. Donnelly C, et al. Client-centred assessment and the identification ofmeaningful treatment goals for individuals with a spinal cord injury. SpinalCord. 2004;42(5):302–7.2. Chen CL, et al. The relationship between sitting stability and functionalperformance in patients with paraplegia. 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Percept MotSkills. 1995;80(2):379–85.•  We accept pre-submission inquiries •  Our selector tool helps you to find the most relevant journal•  We provide round the clock customer support •  Convenient online submission•  Thorough peer review•  Inclusion in PubMed and all major indexing services •  Maximum visibility for your researchSubmit your manuscript atwww.biomedcentral.com/submitSubmit your next manuscript to BioMed Central and we will help you at every step:Chisholm et al. Journal of NeuroEngineering and Rehabilitation  (2017) 14:27 Page 8 of 8

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