simulation of lower limb muscle activity during inclined

IN DEGREE PROJECT MEDICAL ENGINEERING,SECOND CYCLE, 30 CREDITS

, STOCKHOLM SWEDEN 2019

Simulation of lower limb muscle activity during inclined slope walking

GANESH PRASANTH ARUMUGANAINAR

KTH ROYAL INSTITUTE OF TECHNOLOGYSCHOOL OF ENGINEERING SCIENCES IN CHEMISTRY, BIOTECHNOLOGY AND HEALTH

Simulation of lower limb muscleactivity during inclined slopewalking

GANESH PRASANTH ARUMUGANAINAR

Degree Project in Medical EngineeringStockholm, Sweden 2018External Supervisor: Elena Gutierrez-FarewikGroup Supervisor: Rodrigo MorenoExaminer: Mats NilssonReviewer: Svein KleivenSchool of Engineering Sciences in Chemistry, Biotechnology and HealthKTH Royal Institute of Technology

Abstract

Robotic exoskeletons are designed to assist patients with motor dysfunctions.Recent researches focus on extending the robotic assistance to patient activitiesother than ground level walking. This study aims to analyse the lower limbmuscle activity during inclined slope walking contrasting with that of groundlevel walking. Two different angles of inclination were chosen: 9 degrees and18 degrees. 9 degrees inclined slope is the universal ramp size for wheelchairs.The hypothesis is that muscle activation, and ultimately metabolic cost, ininclined slope walking is different from that of ground level walking. Collectedmotion data and simulation in OpenSim prove that the difference in metaboliccost is because of increased activity of ankle dorsiflexors and hip extensors andreduced activity of knee extensors. Finally, muscle activities along with othercriteria such as kinematic alignment and joint range of motion are summed upas biomechanical considerations for robotic exoskeleton design.

Sammanfattning

Robotiska exoskeletoner är utformade för att hjälpa patienter med motoriskdysfunktion. Nyare undersökningar fokuserar på att utöka robotassistansen tillandra patientaktiviteter än grundnivåvandring. Denna studie syftar till attanalysera muskelaktiviteten för nedre extremiteten under gång i lutningjämfört med gång på plan yta. Två olika lutningsvinklar valdes: 9 grader och18 grader. 9 graders lutning är den universella rampstorleken för rullstolar.Hypotesen är att muskelaktivering och slutligen metabolisk kostnad, undergång i lutning, skiljer sig från gång på plan yta. Insamlad rörelsedata ochsimulering i OpenSim bevisar att skillnaden i metabolisk kostnad är på grundav ökad aktivitet av fotled dorsiflexorer och höft extensorer och minskadaktivitet av knäextensorer. Slutligen summeras muskelaktiviteter tillsammansmed andra kriterier som kinematisk inriktning och gemensamt rörelseområdesom biomekaniska överväganden för robotisk exoskelettdesign.

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Acknowledgements

I wholeheartedly thank my supervisor Elena Gutierrez-Farewik for giving mean opportunity to work on this project at KTH Mechanics. I am alwaysgrateful for the passion she showed towards the project and the friendlyambience she created in the group.

I thank my parents, family and friends for their immense love and constantsupport though miles away. A special thanks to my friends who willinglyparticipated as test subjects in my experiment.

My sincere thanks to Mikael Remeringen and Cecilia Lidbeck at theMotoriklab, Astrid Lindgren’s Barnsjukhus for their valuable guidance andprecious time spent in providing the laboratory support.

My wholehearted thanks to Rodrigo Moreno at School of Technology andHealth, KTH for his constructive feedback during group supervision sessions.A special thanks to my thesis reviewer Svein Kleiven.

Annie Charles and Katya Mehyeddine earn a special acknowledgement fortheir valuable time spent in translating my abstract into Swedish.

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ContentsAbstract i

List of Figures vii

List of Tables ix

List of Abbreviations x

1 Introduction 11.1 Aim . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2 Hypothesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

2 Materials and Methods 12.1 Subject . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.2 Experimental Setup . . . . . . . . . . . . . . . . . . . . . . . . . 22.3 Testing Protocol . . . . . . . . . . . . . . . . . . . . . . . . . . . 22.4 Data Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

3 Results 53.1 Considerations at Ankle . . . . . . . . . . . . . . . . . . . . . . . 5

3.1.1 Degrees of Freedom . . . . . . . . . . . . . . . . . . . . . 63.1.2 Range of Motion . . . . . . . . . . . . . . . . . . . . . . . 63.1.3 Muscle Activity . . . . . . . . . . . . . . . . . . . . . . . . 6

3.2 Considerations at Knee . . . . . . . . . . . . . . . . . . . . . . . 83.2.1 Degrees of Freedom . . . . . . . . . . . . . . . . . . . . . 83.2.2 Range of Motion . . . . . . . . . . . . . . . . . . . . . . . 93.2.3 Muscle Activity . . . . . . . . . . . . . . . . . . . . . . . . 9

3.3 Considerations at Hip . . . . . . . . . . . . . . . . . . . . . . . . 93.3.1 Degrees of Freedom . . . . . . . . . . . . . . . . . . . . . 93.3.2 Range of Motion . . . . . . . . . . . . . . . . . . . . . . . 103.3.3 Muscle Activity . . . . . . . . . . . . . . . . . . . . . . . . 10

3.4 Validation of Results . . . . . . . . . . . . . . . . . . . . . . . . . 11

4 Discussion 12

5 Limitations and Future Work 13

6 Conclusion 14

Bibliography 14

Appendices 17

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A Literature Study 17A.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17A.2 Inclined Slope Walking . . . . . . . . . . . . . . . . . . . . . . . . 17

A.2.1 Phases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17A.2.2 Biomechanical Differences . . . . . . . . . . . . . . . . . . 17

A.3 Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19A.3.1 Marker placement . . . . . . . . . . . . . . . . . . . . . . 19A.3.2 Software Tools . . . . . . . . . . . . . . . . . . . . . . . . 19

A.4 Current trends in robotic exoskeletons . . . . . . . . . . . . . . . 20A.4.1 Gait rehabilitation . . . . . . . . . . . . . . . . . . . . . . 20A.4.2 Human locomotion assistance . . . . . . . . . . . . . . . . 21A.4.3 Human strength augmentation . . . . . . . . . . . . . . . 21

A.5 Limitations and challenges . . . . . . . . . . . . . . . . . . . . . . 22A.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23A.7 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

B Muscle Activity Plots 25

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List of Figures2.1 Wooden ramps placed on the force plates . . . . . . . . . . . . . 22.2 Sequence of events in one gait cycle . . . . . . . . . . . . . . . . . 32.3 A screenshot of Lee-Son’s Toolbox interface . . . . . . . . . . . . 43.1 Kinematics at ankle for both trials during ground level walking

and inclined slope walking (Positive values denote dorsiflexionlevels and negative values represent plantarflexion levels. Firstright toe strike at 0 % and second right toe strike at 100% gait,Mid-stance at 23% gait and right heel off at 45% gait.) . . . . . . 5

3.2 Comparison of ankle dorsiflexion and plantarflexion peaks duringground level and inclined slope walking (positive values denotedorsiflexion levels and negative values represent plantarflexionlevels) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

3.3 Comparison of Soleus muscle activity peaks during ground leveland inclined slope walking (The peak values of both the trialsand their mean are given) . . . . . . . . . . . . . . . . . . . . . . 7

3.4 Comparison of Tibialis Posterior muscle activity peaks duringground level and inclined slope walking (The peak values of boththe trials and their mean are given) . . . . . . . . . . . . . . . . . 7

3.6 Comparison of knee flexion and extension peaks during groundlevel and inclined slope walking (positive values denote extensionlevels and negative values represent flexion levels) . . . . . . . . . 8

3.5 Kinematics at knee for both trials during ground level walkingand inclined slope walking (Positive values denote extension levelsand negative values represent flexion levels. First right toe strikeat 0 % and second right toe strike at 100% gait, Mid-stance at23% gait and right heel off at 45% gait.) . . . . . . . . . . . . . . 8

3.7 Comparison of Rectus Femoris muscle activity peaks duringground level and inclined slope walking (The peak values ofboth the trials and their mean are given) . . . . . . . . . . . . . 9

3.8 Kinematics at hip for both trials during ground level walking andinclined slope walking (Positive values denote flexion levels andnegative values represent extension levels. First right toe strikeat 0 % and second right toe strike at 100% gait, Mid-stance at23% gait and right heel off at 45% gait.) . . . . . . . . . . . . . . 10

3.10 Comparison of Adductor Magnus 2 muscle activity peaks duringground level and inclined slope walking (The peak values of boththe trials and their mean are given) . . . . . . . . . . . . . . . . . 11

3.9 Comparison of hip flexion and extension peaks during groundlevel and inclined slope walking (positive values denote flexionlevels and negative values represent extension levels) . . . . . . . 11

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3.11 Comparison of EMG peak values of Rectus Femoris, BicepsFemoris and Tibialis Anterior . . . . . . . . . . . . . . . . . . . . 12

A.1 Markers location (Markers used in the study are framed in red) . 20A.2 Table summarizing currently available lower extremity exoskeletons 22B.1 Soleus muscle activation comparison (Trial 1) . . . . . . . . . . . 25B.2 Soleus muscle activation comparison (Trial 2) . . . . . . . . . . . 25B.3 Tibialis Posterior muscle activation comparison (Trial 1) . . . . . 26B.4 Tibialis Posterior muscle activation comparison (Trial 1) . . . . . 26B.5 Rectus Femoris muscle activation comparison (Trial 1) . . . . . . 27B.6 Rectus Femoris muscle activation comparison (Trial 2) . . . . . . 27B.7 Adductor Magnus muscle activation comparison (Trial 1) . . . . 28B.8 Adductor Magnus muscle activation comparison (Trial 1) . . . . 28

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List of Tables2.1 Anthropometric data of the chosen subject as on the day of

recording . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1A.1 Markers Description . . . . . . . . . . . . . . . . . . . . . . . . . 20

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List of AbbreviationsEMG - ElectromyogramSCI - Spinal Cord InjuryCSV - Comma Separated ValuesCMC - Computed Muscle Control

x

HL205X Degree Project in Medical Engineering

1 IntroductionImproper gaits are a result of stroke, accidents, Parkinson’s, arthritis ormultiple sclerosis[1]. There is a wide possibility for correcting these impropergait patterns through rehabilitation. Among various methods, efficient roboticexoskeleton assistance stands tall. Studies are done to improve thecommunication interface between the user and the robotic exoskeletondevice[5]. Majority of these studies focus on normal gaits but not on othercomplex motions such as walking up inclined slopes. It is important to extendthe robotic assistance to inclined slope walking rather than just ground levelwalking. This particular study aims to analyse lower limb muscle activityduring walking up inclined slopes and contrast the difference with that ofground level walking.

1.1 AimThe aim of this study is to analyse how lower limb muscle activity differs betweenwalking on an inclined slope and ground level walking.

1.2 HypothesisThe hypothesis is that muscle activity, and ultimately metabolic cost, in inclinedslope walking is different from that of ground level walking, and the differencein metabolic cost is because of increased activity of ankle plantarflexors andhip extensors and reduced activity of knee extensors (It is enough to considerone from each group hip flexors/knee extensors and hip extensors/knee flexorsbecause the muscles responsible for hip extension are also responsible for kneeflexion and similarly the muscles responsible for hip flexion are also responsiblefor knee extension).

2 Materials and Methods

2.1 Subject

Table 2.1: Anthropometric data of the chosen subject as on the day of recording

Attribute ValueAge (years) 28Sex (M/F) MHeight (cm) 175Weight (kg) 73Body Mass Index (kg/m2) 23.8

A subject who is healthy, active and having no history of lower extremityinjury was selected from the students of KTH Royal Institute of Technology.

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Anthropometric data of the selected subject were measured on the day ofrecording the motion. The data are tabulated in table 2.1.

2.2 Experimental SetupMotion data are captured in the ’Motorik Lab’ of Astrid Lindgren’sBarnsjukhus, Solna. The laboratory has an approximate 15 meter walkwaywith two Kistler force plates sampled at 1000 Hz frequency in the middle ofthe walkway. Kinematic data of the performed motions are collected by a 8camera VICON Motion Capture System coupled with VICON Nexus software.For inclined slope walking, wooden ramps of two different angles of inclination:9 degrees and 18 degrees were constructed. They were placed on force platesas shown in figures 2.1(a) and 2.1(b). Force plates are neutralised after placingthe ramps so that weights of the wooden pieces are eliminated during thecalculation of external loads.

Electromyogram (EMG) data are also recorded with an aim of validating theresults obtained from OpenSim later. The EMG data are acquired throughNoraxon DTS EMG sensor system, which is coupled with VICON Nexussoftware. Three different lower limb muscles are chosen for EMG: RectusFemoris (a hip flexor and knee extensor), Biceps Femoris (a hip extensor andknee flexor) and Tibialis Anterior (an ankle dorsiflexor). The EMG sensors areplaced on the skin as per the directions given by Surface ElectroMyoGraphyfor the Non-Invasive Assessment of Muscles (SENIAM)[21].

(a) 9 degree inclination (b) 18 degree inclination

Figure 2.1: Wooden ramps placed on the force plates

2.3 Testing ProtocolThe test subject was prepared with 23 markers placed on trunk and lowerlimbs as per Plug-in Gait model requirements[10] and three EMG sensors onthe three different muscles as mentioned earlier. Table A.1 in the appendixrepresents the 23 markers used and figure A.1 shows the location of thosemarkers. The subject walked several times on the walkway and the ramps to

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get comfortable with.

The subject with placed markers and EMG sensors was allowed to performthree different motions: ground level walking, walking up 9 degrees inclinedslope and walking up 18 degrees inclined slope. The subject was made to placethe right foot on the first force plate when walking. Seven trials were recordedfor each motion. Recorded trials were discarded if the foot was not fully placedon the plates or the subject forcefully altered the walking pattern in order toplace the right foot on the right plate. Two successful trials for the chosensubject are considered for analysis. One gait cycle is considered as thesequence of events from first right toe strike to second right toe strike asshown in figure 2.2[23]. So as per the considerations, analysis are done for thesequence of events from first right toe strike on first force plate, left toe strikeon second force plate and second right toe strike on the walkway. For a healthyindividual, gait cycle is symmetrical about the sagittal plane and henceconsidering only the right gait cycle for analysis proves to be sufficient[24].

Figure 2.2: Sequence of events in one gait cycle

2.4 Data ProcessingVICON Nexus collects motion data of the 23 markers and the EMG data fromthe 3 sensors placed on the subject. The collected trial needs to bereconstructed first. There is a higher probability that the marker informationmight go missing in some time frames during the trial due to the quality of thecamera system or any interference that blocked the camera from capturing themarker data completely. If the trials are exported with unlabelled markers andunfilled gaps, OpenSim would not be able to simulate the desired kinematicsand kinetics. For these reasons, the quality of the collected trial needsreviewing and processing prior to be exported from VICON Nexus. Thecollected trial is reviewed to find if there are any unlabelled markers andmissing trajectories of the markers. The amount of processing requireddepends upon the quality of the captured data[15]. Automatic labelling toollabels all the markers. Still one or more marker might go missing and needsmanual labelling. Once the markers are labelled, gaps in the motion path ofeach marker are to be identified and filled. There are both automatic andmanual options for gap filling the trajectories. Auto-gap fill option works bestfor smaller gaps. It is advisable to use manual gap filling for larger gaps[16].There are five different gap filling tools available for manual gap filling: Splinefill, pattern fill, Rigid body fill, Kinematic fill, Cyclic fill. Suitable gap filling

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tool that works best for the particular case is chosen as per the guidelines fromVICON Nexus User Guide[16]. Once the trials of our interest are completelylabelled and gaps filled, they are exported from VICON Nexus as CommaSeparated Values ’.CSV’ file.

This ’.CSV’ file contains the whole trajectory information of the 23 markers,force plate data and EMG data. This ’.CSV’ file needs to be converted toOpenSim usable file formats. Lee-Son’s Toolbox is an open-source toolkit thatadapts to the number of markers, force plates and the global coordinates andeasily convert them to ’.trc’ file containing marker trajectories and ’.mot’ filecontaining force plate data which can be used directly in OpenSim. Ascreenshot of the Lee-Son Toolbox interface is shown in the figure 2.3. This isa user-friendly toolkit that adapts to OpenSim and laboratory coordinate axesand the number of markers and convert ’.CSV’ files into OpenSim usable fileformats[18].

Figure 2.3: A screenshot of Lee-Son’s Toolbox interface

OpenSim is an open-source musculoskeletal modelling platform[14]. Thevarious tools available in OpenSim and what they are meant for are explainedin the appendix A.3.2. Computed Muscle Control (CMC) tool in OpenSimcomputes muscle excitation levels necessary to drive the model to perform thedesired kinematics as per the marker data in the presence of external loads asper the force plate data.

A brief explanation of how CMC tool algorithm works is given below. Thefirst step is to compute desired accelerations necessary to drive themusculoskeletal model to perform the desired kinematics. Since, there cannotbe an instantaneous change in the muscle forces applied to the body, desiredaccelerations are computed for a specific time interval (usually 0.01 seconds)which is long enough for the muscle forces to change. The next step in CMC isto compute the actuator controls that achieve the previously computed desiredaccelerations. The final step of CMC is to conduct a forward dynamic

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simulation advancing forward in time. The CMC algorithm is iterative wherethese three steps are repeated until the end of desired movement.

Output from CMC execution has the computed kinematics at joints andactivity of individual muscles during the desired motions which are thenplotted and compared in Matlab. While plotting, only the peak values mattermore than the activity pattern so as to decide the activity levels of muscles.Moreover, the designers of robotic exoskeleton too consider the peaks for sizingtheir actuators[22].

3 ResultsGround level walking differs from inclined slope walking in terms of kinematicsat hip, knee and ankle. The designers of robotic exoskeleton would be interestedin degrees of freedom, range of motion and muscular activity associated witheach joint in order to design a robotic exoskeleton that complies with humanlimb.

3.1 Considerations at Ankle

(a) Trial 1 (b) Trial 2

Figure 3.1: Kinematics at ankle for both trials during ground level walking andinclined slope walking (Positive values denote dorsiflexion levels and negative valuesrepresent plantarflexion levels. First right toe strike at 0 % and second right toe strikeat 100% gait, Mid-stance at 23% gait and right heel off at 45% gait.)

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Figure 3.2: Comparison of ankle dorsiflexion and plantarflexion peaks during groundlevel and inclined slope walking (positive values denote dorsiflexion levels and negativevalues represent plantarflexion levels)

3.1.1 Degrees of Freedom

Ankle is a hinge joint that has only one degree of freedom- dorsiflexion andplantarflexion.

3.1.2 Range of Motion

Figure 3.1 shows the ankle angle variation over percent gait cycle for the twotrials considered (positive values denote dorsiflexion levels and negative valuesrepresent plantarflexion levels). Also, it is observed that the range of motionfor ankle goes from -14.748 to 9.278 degrees for ground level, from -15.510 to15.342 degrees for walking up 9 degree slope and from -15.045 to 34.37 degrees forwalking up 18 degree slope. Also, the dorsiflexion and plantarflexion levels aregreater for walking up 18 degree inclined slope. The dorsiflexion level increaseswith increasing slope with walking up 18 degree slope being the highest andground level walking being the lowest. Whereas, the plantarflexion level isalmost the same for ground level walking and walking up 9 degree inclinedslope. The ankle plantarflexes more during walking up 18 degree inclined slope.

3.1.3 Muscle Activity

The muscle activity associated could be analysed by considering two differentmuscles responsible for ankle plantarflexion: Soleus (figure 3.3) and TibialisPosterior (figure 3.4). In figures 3.3 and 3.4, it is clearly observed that the muscleactivity peak is the highest for walking up 18 degree slope and subsequentlydecreases with decrease in slope which is in accordance with the kinematicresults shown in figures 3.1 and 3.2. The smaller standard deviation of Soleusmuscle activity during walking up 18 degree inclined slope could be seen as a

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result of the subject recreating the same level of ankle movement in both thetrials.

Figure 3.3: Comparison of Soleus muscle activity peaks during ground level andinclined slope walking (The peak values of both the trials and their mean are given)

Figure 3.4: Comparison of Tibialis Posterior muscle activity peaks during groundlevel and inclined slope walking (The peak values of both the trials and their meanare given)

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Figure 3.6: Comparison of knee flexion and extension peaks during ground leveland inclined slope walking (positive values denote extension levels and negative valuesrepresent flexion levels)

3.2 Considerations at Knee


Figure 3.5: Kinematics at knee for both trials during ground level walking andinclined slope walking (Positive values denote extension levels and negative valuesrepresent flexion levels. First right toe strike at 0 % and second right toe strike at100% gait, Mid-stance at 23% gait and right heel off at 45% gait.)

3.2.1 Degrees of Freedom

Knee is a condyloid joint and has two rotational degrees of freedom-flexion/extension and internal/external rotation. However, previous literatureconsider only flexion/extension as the only one degree of freedom due toreduced internal/external rotations at knee.

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Figure 3.5 shows the knee angle variation over percent gait cycle for the two trialsconsidered (positive values denote extension levels and negative values representflexion levels). Also, it is observed that the range of motion for knee goes from-52.413 to 1.546 degrees for ground level, from -58.007 to -2.454 degrees forwalking up 9 degree slope and from -79.695 to -5.143 degrees for walking up 18degree slope. In figure 3.5, it is observed that the knee flexion is the greatest forwalking up 18 degree inclined slope and subsequently decreases with decease inslope. In figure 3.6, it is observed that the knee extension is the highest duringground level walking and it decreases with increase in slope. This is because thepresence of wooden ramp restricts knee from fully extending, thereby reducingthe knee extension angle.


The muscle activity associated could be analysed by considering RectusFemoris, a knee extensor muscle. In figure 3.7, it is evident that the muscleactivity is the highest for ground level walking and it decreases with increasein slope which is in accordance with the kinematic results shown in figures 3.5,3.6. Knee extension activity is reduced for increasing slopes of inclination(figures 3.5 and 3.6) and so the muscle activity associated with it.

Figure 3.7: Comparison of Rectus Femoris muscle activity peaks during ground leveland inclined slope walking (The peak values of both the trials and their mean aregiven)

3.3 Considerations at Hip3.3.1 Degrees of Freedom

Hip is a ball and socket joint that has three degrees of freedom-flexion/extension, adduction/abduction and internal/external rotation.

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However, for convenience only flexion/extension of hip are analysed in detailand other two degrees of freedom bring scope for future study.


Figure 3.8 shows the hip angle variation over percent gait cycle for the two trialsconsidered (positive values denote flexion levels and negative values representextension levels). Also, it is observed that the range of motion for hip goes from-23.156 to 16.303 degrees for ground level, from -14.338 to 37.904 for walking up9 degree slope and from -8.26 to 60.94 for walking up 18 degree slope. Figure3.8 shows that hip flexion is the lowest for ground level walking and it increaseswith increase in slope. Also, that hip extension is the highest for ground levelwalking and it decreases with increase in slope.


The muscle activity associated with this could be analysed by consideringAdductor Magnus 2, a hip extensor. Recent studies focus on Adductor Magnusextending its function for activities other than adduction. This gainedattraction and that’s the reason why Adductor Magnus is chosen over otherhip muscles for analysis. The activity peak of Adductor Magnus is almost thesame for ground level walking and inclined slope walking when observedvisually. However, their comparison is explained statistically in the’Discussion’ section.


Figure 3.8: Kinematics at hip for both trials during ground level walking and inclinedslope walking (Positive values denote flexion levels and negative values representextension levels. First right toe strike at 0 % and second right toe strike at 100%gait, Mid-stance at 23% gait and right heel off at 45% gait.)

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Figure 3.10: Comparison of Adductor Magnus 2 muscle activity peaks during groundlevel and inclined slope walking (The peak values of both the trials and their meanare given)

Figure 3.9: Comparison of hip flexion and extension peaks during ground leveland inclined slope walking (positive values denote flexion levels and negative valuesrepresent extension levels)

3.4 Validation of ResultsIn order to validate the results obtained from OpenSim, EMG is acquired fromthree different muscles: Rectus Femoris, Biceps Femoris and Tibialis Anteriorto confirm the activity levels. The EMG results coincided with the OpenSimresults. A graph comparing the mean of peak EMG values of the abovementioned three muscles during ground level walking and inclined slopewalking for two trials is shown in figure 3.11. It is evident that the activity ofBiceps Femoris, a hip extensor and Tibialis Anterior, an ankle dorsiflexor has

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increased with increase in slope whereas the activity of Rectus Femoris, a kneeextensor has decreased with increase in slope.

Figure 3.11: Comparison of EMG peak values of Rectus Femoris, Biceps Femorisand Tibialis Anterior

4 DiscussionConsidering the activity of Soleus, there is a 3.57% increase in the activitylevel during walking up 9 degree inclined slope compared to that of groundlevel walking. Whereas, there is a whopping 78.5% increase in activity duringwalking up 18 degree inclined slope compared to ground level walking. Thisincrease in muscle activity levels are statistically significant with (p<0.45) and(p<0.11) respectively.

Considering the activity of Tibialis Posterior, there is a 48.5% increase inactivity during walking up 9 degree inclined slope compared to ground levelwalking. There is a 54.2% increase in activity during walking up 18 degreeinclined slope compared to that of ground level walking. This increase inmuscle activity levels are found to be statistically significant with (p<0.13)and (p<0.11) respectively. Soleus and Tibialis Posterior are ankleplantarflexors and this increase in activity level is in accordance with the ankleplantarflexion angles in figure 3.1. This could be due to the increased ankledorsiflexion levels during inclined slope walking due to the slope of ramps.This in turn causes the plantarflexors to activate more for plantarflexing theankle.

Considering the activity of Rectus Femoris, there is a 7.14% decrease duringwalking up 9 degree slope and a 10% decrease during walking up 18 degreeinclined slope when compared to ground level walking. This decrease in muscleactivity levels are found to be statistically significant with (p<0.42) and(p<0.39) respectively. The larger values of alpha in this case may be due to

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the consideration of less number of samples and it might change when morenumber of trials are considered. The slope of the ramp restricts the knee fromextending completely and hence the activity of the knee extensor decreaseswith increase in slope. This result is in accordance with the EMG resultsshown in figure 3.11 and kinematic results shown in figure 3.6.

Considering the activity of Adductor Magnus 2, it is difficult to generalise thedifference in activity with respect to slope since the there is no much differenceseen visually. However, the muscle activity during walking up 9 degree slope isgreater than that of ground level walking with statistical significance (p<0.46).Also, the muscle activity during walking up 18 degree slope is greater thanthat of ground level walking with statistical significance (p<0.54). Withgreater significance values, the results may not be generalised at this point butin future with more trials or subjects considered, this activity difference couldbe seen prominent and that would help to conclude how the muscle activity athip varies with respect to slope.

With these simulated OpenSim results, validation through acquired EMG dataand statistical hypothesis testing, proposed hypotheses are verified. Thedifference in metabolic cost between ground level walking and inclined slopewalking is due to the fact that the ankle plantarflexors have an increasedmuscle activity and knee extensors have a decreased muscle activity duringinclined slope walking compared to ground level walking. For muscle activityat the hip, the results are not convincing enough to generalise the hypothesisat this point.

5 Limitations and Future WorkOne of the biggest limitation of the work is that the study is performed ononly one healthy subject considering only two valid trials. Had more trialsbeen considered, the results would have become more reliable. Anotherlimitation is that the statistical significance proving the difference in muscleactivity levels is higher comparing to the usual 0.05. This might also be aneffect of considering less number of trials. Had more number of trials beenconsidered, the difference in muscle activity levels would be more prominentthereby giving a better statistical significance level. The overall idea is toprovide information to develop a robotic exoskeleton that assists patients withmotor dysfunctions in performing complex motions like climbing up aninclined slope. So, there are also biomechanical considerations other thandegrees of freedom, range of motion and muscle activity at individual joints tobe considered such as joint torque, its application at the right time with rightintensity and the velocity with which the joint angles change. Also, it isnecessary to study how patients with muscle weaknesses and otherabnormalities adapt to similar kind of motion. Finally, it is necessary to studyother muscle activities too in order to analyse the combined activity at

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individual joints. When these factors are considered and analysed, the roboticexoskeleton shall be designed in such a way that it provides torque to the limbjoints whenever necessary thereby assisting patients with motor dysfunctionsin climbing up inclined slopes.

6 ConclusionThis study is aimed to analyse the difference in lower limb muscle activityduring ground level walking and walking up an inclined slope. Simulationresults from OpenSim software clearly show that muscle activity of ankleplantarflexors has increased with increase in slopes whereas the activity ofknee extensors has decreased with increase in slopes. These results are inaccordance with the acquired EMG data from three different muscles and alsofound to be statistically significant. And this verifies the proposed hypothesis.Further research is necessary in analysing other factors such as the combinedmuscle activity at a joint, joint torque and velocity with which the joint anglechanges thereby predicting the requirements for a robotic exoskeleton to assistpatients with motor dysfunctions in performing the desired complex motionssuch as walking up inclined slopes.

Bibliography[1] Patricia Krawetz, MD, Patricia Nance, MD. Gait Analysis of Spinal CordInjured Subjects: Effects of Injury Level and Spasticity. Arch Phys MedRehabil Vol 77, July 1996.

[2] Amanda Ortiz. The metabolic cost of walking and running up a 30 degreeincline: implications for vertical kilometer foot races. Undergraduate HonorsTheses. 2017

[3] Maxim N. Nikolenko, Denis A. Kotin. General Principles of MedicalExoskeleton Design. 18th International Conference on Micro/Nanotechnologiesand Electrical Devices. 2017.

[4] Ian Benson, Kirsten Hart1, Dot Tussler and Joost J van Middendorp.Lower-limb exoskeletons for individuals with chronic spinal cord injury:findings from a feasibility study. Clinical Rehabilitation 2016, Vol. 30(1)73–84.

[5] Chunjie Chen, Xinyu Wu, Du-xin Liu, Wei Feng and Can Wang. Designand Voluntary Motion Intention Estimation of a Novel Wearable Full-BodyFlexible Exoskeleton Robot. Hindawi Mobile Information Systems Volume2017.

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[6] Sadiq J. Abbas and Zahraa M. Abdulhassan. Kinematic Analysis ofHuman Climbing up and Down Stairs at Different Inclinations. Eng.Tech.Journal, Vol. 31,Part (A), No.8, 2013.

[7] Emilie Desrosiers, Cyril Duclos and Sylvie Nadeau. Gait adaptation duringwalking on an inclined pathway following spinal cord injury. ClinicalBiomechanics 29 (2014) 500–505.

[8] Bing Chen, Hao Ma, Lai-Yin Qin, Fei Gao, Kai-Ming Chan,Sheung-WaiLaw, Ling Qin, Wei-Hsin Liao. Recent developments and challenges of lowerextremity exoskeletons. Journal of Orthopaedic Translation (2016) 5, 26-37.

[9] Kotaro Sasaki, Richard R Neptune. Muscle mechanical work and elasticenergy utilization during walking and running near the preferred gaittransition speed. Gait Posture 23 (2006) 383–390.

[10] Vicon Motion System. Modeling with Plug-in Gait. 2018.URL:https://docs.vicon.com/display/Nexus25/Modeling+with+Plug-in+Gaithttps://docs.vicon.com/display/Nexus25/Modeling+with+Plug-in+Gait.

[11] National Center for Simulation in Rehabilitation Research. Gait 2392 and2354 Models. URL: https://simtk-confluence.stanford.edu/display/OpenSim/Gait+2392+and+2354+Models.

[12] National Center for Simulation in Rehabilitation Research. OpenSimUser’s Guide. 2017. URL:https://simtk-confluence.stanford.edu/display/OpenSim/User%27s+Guide

[13] Felipe Costa Alvim. c3d2OpenSim. 2014. URL:https://simtk.org/projects/c3d2opensim.

[14] Guide to OpenSim Workflow and Tools. 2018. URL: https://simtk-confluence.stanford.edu/display/OpenSim/Guide+to+OpenSim+Workflow+and+Tools.

[15] Nexus 2.5 VICON Documentation Pipeline Tools. 2018. URL:https://docs.vicon.com/display/Nexus25/Pipeline+tools.

[16] VICON Nexus User Guide. 2016. URL:https://docs.vicon.com/display/Nexus25/Vicon+Nexus+User+Guide.

[17] Paul DeVita et al. Muscles do more positive than negative work in humanlocomotion. J Exp Biol. 2007 October ; 210(Pt 19): 3361–3373.doi:10.1242/jeb.003970.

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[18] Lee Sang Yun, Jinkyou Son. Lee-Son’s Toolbox User Manual. URL:https://simtk.org/home/lee-son/.

[19] Tim Dorn. C3D Extraction Toolbox. URL:https://simtk.org/home/c3dtoolbox by Tim Dorn.

[20] Peter Loan. OpenSMAC: Utility for importing Motion Analysis data(TRB, ANB) into OpenSim. URL: https://simtk.org/home/opensmac.

[21] Seniam Group. SENIAM Project. http://www.seniam.org/.

[22] Massimo Cenciarini and Aaron M. Dollar. Biomechanical Considerationsin the Design of Lower Limb Exoskeletons. IEEE International Conference onRehabilitation Robotics. 2011.

[23] Walking in Graphs. University of Dallas. URL:https://www.utdallas.edu/atec/midori/Handouts/walkingGraphs.htm

[24] Kadek Heri Sanjaya et al. The biomechanics of walking symmetry duringgait cycle in various walking condition. Proceedings of 2016 1st InternationalConference on Biomedical Engineering: Empowering Biomedical Technologyfor Better Future, IBIOMED 2016, , art. no. 7869813.

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Appendices

A Literature Study

A.1 IntroductionThe report is an overall idea gained from the literature survey performed tounderstand how ground level walking differs from inclined slope walking interms of lower limb muscle activity to predict the requirements for a roboticexoskeleton to assist patients with motor dysfunctions.

The first section insists on a theoretical explanation of how walking on aninclined slope differs from level ground walking. It also speaks about howinclined slope walking differs biomechanically between healthy subjects andsubjects with motor dysfunctions. The next section gives an account ondifferent tools used in motion capturing, modeling and processing. The thirdsection focuses on currently available exoskeleton models. The next sectionincludes the limitations of existing models and challenges expected indesigning a new model.

A.2 Inclined Slope WalkingA.2.1 Phases

Unlike level ground walking, the phases of walking are different for walking upan inclined plane and walking down a declined plane.

In inclined plane walking, the body first gets ready to be pulled up during theweight acceptance phase. Next is the pull up phase where there is aprogression of ascending from one step to another. The step is completed inthe forward continuance phase. Next is the foot clearance phase where the legis brought up for the next step. The final phase is the foot placement phasewhere the leg is placed and the second step is completed[6].

Whereas declined plane walking involves weight acceptance phase (bodyprepares to pull down), forward continuance phase (body starts to moveforward), controlled lowering (descending from one step to another), leg pullthrough (leg swing through) and foot placement[6].

A.2.2 Biomechanical Differences

Walking on an inclined slope is a complex activity that requires musclestrength, coordination and body balance[2]. Physiologically, climbing up anddown an inclined slope is more challenging and energy demanding than ground

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level walking. This is because the body has to lift its weight against gravity[2].The connective tissues and tendons store these gravitational potential andkinetic energies as elastic energy. This elastic energy is released as positivework done when required later[9]. The main biomechanical difference is thatelastic energy stored in tendons is almost fully recovered in ground levelwalking. But in walking up and down an inclined slope, the level of elasticenergy that can be stored is diminished. To compensate this, the bodygenerates a net positive mechanical energy to raise the centre of mass. This isthe main reason for the increase in metabolic energy cost when climbing upand down an inclined slope when compared to ground level walking. Moreover,it is estimated that this excess metabolic energy cost is compensated by theincrease in net work done at the hip only, while, the performance of knee andankle remained the same in all inclines. But it does not mean that only hipextensor muscles produce this excess power. There is also a possibility of kneeextensors producing this work by contracting.

For healthy individuals, in ground level walking, the metabolic cost remainsminimum when walked at an optimum speed. Whereas, it costs more whenwalked at a lower or higher speed. Muscle activation duration and magnitudeare expected to increase in inclined plane walking which in turn causesincreased metabolic cost. Due to these differences between ground levelwalking and walking up and down an inclined slope, the biomechanics of thesemovements are different at joint and muscle levels.

Subjects with motor dysfunctions such as partial spinal cord injury subjectshave weak knee extensors and hip muscles that limit their walking abilities[7].It is also dependant on other factors such as spasticity, posture and loss ofproprioception. Generally, the walking pattern of SCI subjects differ from thatof normal, healthy subjects in a number of ways:

• Decrease in step length

• Increase in double limb support time

• Increased knee flexion

• Abnormal knee-hip coordination

During voluntary contraction, these subjects exhibit less peak isometric torquein the muscle groups of knee and plantar flexors. Considering ankle of thesesubjects, there is a deficiency in propulsion that leads to inadequate push-offduring walking[7].

Emilie et al. compared the gait cycle parameters of healthy and SCI subjectsand found that SCI subjects walk with much less gait speed, cadence andstride length compared to healthy subjects at normal gait speed as well asslow gait speed. They also found that power peak values at ankle, hip and

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knee of healthy subjects at slow gait speed were almost similar to those of SCIsubjects without any much significant change. Whereas, a significant changewas seen in power peak values of healthy subjects at normal gait speedcompared to those of SCI subjects. These results are mainly due to motordeficit at the ankle which agrees with weaker push-off as previously discussed.

During uphill walking, healthy subjects tend to increase their step length andreduce their cadence[7]. At heel strike, there is an increase in knee flexion.During stance phase, there is an increase in leg extension. There is also anincrease in propulsion, moments of hip extensor and plantar flexors. Whereas,SCI subjects have an increased hip and trunk flexion. Also, there is a weakpush off at the end of stance phase[7].

During downhill walking, healthy subjects tend to decrease their step lengthand increase their cadence[7]. During stance phase, there is an increase in kneeflexion, increase in the activity of knee extensors and dorsiflexors and breakingforce. Moreover, healthy subjects tend to show a back tilt of the trunk,linearly depending on the descending slope, which provides an additionalbalance in downhill walking. Whereas, SCI subjects, due to constant trunkflexion position, tend to show a reduced adaptation to downhill walking.

A.3 ToolsA.3.1 Marker placement

Capturing motion data requires a motion lab equipped with camera and forceplates system. The subject has to be prepared with markers placed beforerecording the motion. As per literature survey, 8 camera Vicon MotionCapturing System is coupled with a software Vicon Nexus which uses Plug-inGait model that requires 23 markers placed on trunk and lower limbs of thesubject[10]. Table 3.1 represents the 23 markers used and figure 3.1 shows thelocation of those markers.

A.3.2 Software Tools

VICON Nexus: Plug-in Gait Dynamic Model was used in Vicon Nexusmotion acquisition. The model requires information of 23 markers on trunkand lower limbs[10]. Preprocessing involves automatic gap filling whichcorrects missing markers and ensures accuracy of collected data.

Matlab: Vicon Nexus exports motion data files as ’.c3d’ files. A specialMatlab extraction tool called ’c3d2OpenSim’ tool is modified as per ourrequirements to extract ’.trc’ file that contains static and dynamic markerdata and ’.mot’ file that contains ground reaction force data[13]. These ’.trc’and ’.mot’ files are the inputs for the simulation in OpenSim.

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Table A.1: Markers Description

Marker DescriptionC7 7th cervical vertebraT10 10th thoracic vertebraCLAV ClavicleSTRN SternumRBAK Right BackLSHO/RSHO Left/Right ShoulderLASI/RASI Left/Right ASISLPSI/RPSI Left/Right PSISLTHI/RTHI Left/Right ThighLKNE/RKNE Left/Right KneeLTIB/RTIB Left/Right TibiaLANK/RANK Left/Right AnkleLHEE/RHEE Left/Right HeelLTOE/RTOE Left/Right Toe

Figure A.1: Markers location(Markers used in the study are framedin red)

OpenSim: Processing in OpenSim includes scaling, inverse kinematics,inverse dynamics, residual reduction and static optimization. Scaling scalesthe generic musculoskeletal model to the subject dimensions. Inversekinematics computes joint angles for the model that best produce the motionof the subject. Inverse dynamics computes net joint reaction forces and netjoint moments using previously calculated joint angles, angular velocities andangular accelerations of the model and also experimental ground reactionforces and moments. Residual reduction algorithm alters the torso centre massand allows the kinematics to be more dynamically consistent with the groundreaction force data. Static optimization works on inverse kinematics resultsand calculates individual muscle activation and force at each instance oftime[12].

Microsoft Excel: Static optimization gives individual muscle activation in’.sto’ file which shall be opened in MS Excel. Comparison of different muscleactivation for different kinds of motions are time normalised and plotted againstpercent gait cycle. Plotting shall be done in OpenSim directly but MS Excelproves to be more convenient in formatting the plots.

A.4 Current trends in robotic exoskeletonsAt present, robotic exoskeletons are designed and manufactured for three mainareas of application: gait rehabilitation, human locomotion assistance andhuman strength augmentation.

A.4.1 Gait rehabilitation

Rehabilitation trainings are given to patients who lost their normal gaitpattern due to neurological injuries such as stroke and spinal cord injuries.

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The problem with the already existing trainings is that they requiredenormous man power from the therapists and also proved to be timeconsuming and inefficient[8]. Robotic exoskeletons replaced these conventionaltrainings. They provided intensive motion for the patients and their recoverycould be estimated by analyzing the measured torque values by the sensorsthereby enabling the therapists to concentrate more on the gait patternanalysis.

Robotic Orthosis Lokomat is a system developed by Hocoma, Switzerland.The system is composed of a robotic orthosis with four degrees of freedom.Assistive torque in knee and hip are provided by actuators. Force sensorsmeasure these torque values. It is proved to be effective in gaitrehabilitation[8].

Active Leg Exoskeleton (ALEX) is developed by Banala et al., University ofDelaware. This system is improvised with seven degrees of freedom. Itseffectiveness has been proved in stroke recovered patients that their gaitpatterns looked closer to those of healthy individuals[8].

Ekso GT is an exoskeleton developed by Ekso Bionics, USA. This system isbased on ‘variable assist’ that is, it provides assistance based on the needs ofthe particular patient[8].

A.4.2 Human locomotion assistance

Locomotion assistance is provided for the patients who have completely losttheir lower limb mobility as a result of paralysis. These exoskeletons aredesigned to provide external torque at joints of these patients.

The ReWalk exoskeleton developed by ReWalk Robotics, USA provides hipand knee motion power to enable SCI subjects to stand upright and walk. Thedevice senses the upper body forward tilt and then mimics the gait pattern ofa healthy individual[8].

The Vanderbilt exoskeleton built by Goldfarb et al. is the first of its kindto assist SCI patients in standing up, sitting down and walking up and downstairs. There are proven results where the hip and knee joint amplitudes arefound closer to that of non-SCI subjects[8].

A.4.3 Human strength augmentation

There is a need to enhance natural ability for people like soldiers, firefighters,relief workers to perform heavy works such as carrying loads which theycannot perform with their natural ability.

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Berkeley Lower Extremity Exoskeleton (BLEEX) was developed by Universityof California to help soldiers carry heavy loads. Results show that soldierswere able to walk at 1.3 m/s carrying a load of 34 kg.

University of Tsukuba, Japan developed Hybrid Assistive Limb (HAL) whichwas released in several versions such as full body, two leg and single legversions. The full body version helped in carrying a maximum load of 70 kg.

Different exoskeletons currently available are summarized in table 1 shownbelow.

Figure A.2: Table summarizing currently available lower extremity exoskeletons

A.5 Limitations and challengesThe general working of an intention based robotic exoskeleton involves user’smotion data acquisition and analysis and assisting based on the user’s intention.There are different types of biomechanical data associated with human motion.They are

• kinematic data (body posture and joint angles)

• kinetic data (human joint torque, ground reaction forces, and interactionforce between user and exoskeleton)

• bioelectric data (EMG signals and brain signals)

These data are acquired by different motion sensors. The user’s intention ispredicted by detecting the changes occurred in these data.

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Limitations include poor portability due to heavy weight. Some usermovements are still unachievable. Affordability is another concern. Cost needsto be effectively reduced. Some mechanical designs alter the normal gaitpattern and thereby causing high metabolic cost and discomfort to the users.The design should be customizable depending on the user’s requirements.Noise which the device makes adds to discomfort of the user and thesurrounding. Though present exoskeleton technologies could achieve motionslike sitting, standing and walking on a level plane, there is still a vast area tobe discovered, such as, walking on an uneven surface, stepping into and out ofa car and so on[8].

Future work in exoskeletons mainly aims in making the device less weight andeasy to port. This requires replacing the used material with other which haslow density and toughness. High efficient actuators with high power to weightratio shall be used. Integrating motors, clutches and brakes into a single deviceshall be investigated. Much work is still needed in the noiseless acquisition ofEMG signals from the user. Researches should be carried out in reducing thecost of the device and making the device available for everyone[8].

A.6 SummaryThe literature study, so far, focused on the biomechanical differences of groundlevel walking and inclined slope walking, gait cycle parameters of healthy andmotor impaired subjects, an overview of software tools which might be of useduring the thesis project, currently available robotic exoskeletons and their prosand cons. With some more investigation on muscle activities and metaboliccost of inclined slope walking, it would be possible to design an intention basedrobotic exoskeleton that overcomes the existing cons and challenges.

A.7 References[1] Patricia Krawetz, MD, Patricia Nance, MD. Gait Analysis of Spinal CordInjured Subjects: Effects of Injury Level and Spasticity. Arch Phys MedRehabil Vol 77, July 1996.

[2] Amanda Ortiz. The metabolic cost of walking and running up a 30 degreeincline: implications for vertical kilometer foot races. Undergraduate HonorsTheses. 2017

[3] Maxim N. Nikolenko, Denis A. Kotin. General Principles of MedicalExoskeleton Design. 18th International Conference on Micro/Nanotechnologiesand Electrical Devices. 2017.

[4] Ian Benson, Kirsten Hart1, Dot Tussler and Joost J van Middendorp.Lower-limb exoskeletons for individuals with chronic spinal cord injury:findings from a feasibility study. Clinical Rehabilitation 2016, Vol. 30(1)

23


73–84.

[5] Chunjie Chen, Xinyu Wu, Du-xin Liu, Wei Feng and Can Wang. Designand Voluntary Motion Intention Estimation of a Novel Wearable Full-BodyFlexible Exoskeleton Robot. Hindawi Mobile Information Systems Volume2017.

[6] Sadiq J. Abbas and Zahraa M. Abdulhassan. Kinematic Analysis ofHuman Climbing up and Down Stairs at Different Inclinations. Eng.Tech.Journal, Vol. 31,Part (A), No.8, 2013.

[7] Emilie Desrosiers, Cyril Duclos and Sylvie Nadeau. Gait adaptation duringwalking on an inclined pathway following spinal cord injury. ClinicalBiomechanics 29 (2014) 500–505.

[8] Bing Chen, Hao Ma, Lai-Yin Qin, Fei Gao, Kai-Ming Chan,Sheung-WaiLaw, Ling Qin, Wei-Hsin Liao. Recent developments and challenges of lowerextremity exoskeletons. Journal of Orthopaedic Translation (2016) 5, 26-37.

[9] Kotaro Sasaki, Richard R Neptune. Muscle mechanical work and elasticenergy utilization during walking and running near the preferred gaittransition speed. Gait Posture 23 (2006) 383–390.

[10] Vicon Motion System. Modeling with Plug-in Gait. 2018.URL:https://docs.vicon.com/display/Nexus25/Modeling+with+Plug-in+Gaithttps://docs.vicon.com/display/Nexus25/Modeling+with+Plug-in+Gait.

[11] National Center for Simulation in Rehabilitation Research. Gait 2392 and2354 Models. URL: https://simtk-confluence.stanford.edu/display/OpenSim/Gait+2392+and+2354+Models.

[12] National Center for Simulation in Rehabilitation Research. OpenSimUser’s Guide. 2017. URL:https://simtk-confluence.stanford.edu/display/OpenSim/User%27s+Guide

[13] Felipe Costa Alvim. c3d2OpenSim. 2014. URL:https://simtk.org/projects/c3d2opensim.

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B Muscle Activity Plots

Figure B.1: Soleus muscle activation comparison (Trial 1)

Figure B.2: Soleus muscle activation comparison (Trial 2)

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Figure B.3: Tibialis Posterior muscle activation comparison (Trial 1)

Figure B.4: Tibialis Posterior muscle activation comparison (Trial 1)

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Figure B.5: Rectus Femoris muscle activation comparison (Trial 1)

Figure B.6: Rectus Femoris muscle activation comparison (Trial 2)

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Figure B.7: Adductor Magnus muscle activation comparison (Trial 1)

Figure B.8: Adductor Magnus muscle activation comparison (Trial 1)

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TRITA CBH-GRU-2019:002

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simulation of lower limb muscle activity during inclined

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