Clinical Gait Data Processing and the Assessment of Gait Abnormalities within aNeurorehabilitation Program

submitted in partial fulfillment for the degree of master of science

Callum Hsiao12850748

master information studiesdata science

faculty of scienceuniversity of amsterdam

2020-11-26

Internal Supervisor External Supervisor UvA ExaminerTitle, Name Dr Erik Bekkers Ruud van der Veen Dr Frank NackAffiliation UvA, AMLab Daan Theeuwes Centrum UvAEmail e.j.bekkers@uva.nl r.vanderveen@daantheeuwescentrum.nl nack@uva.nl

Clinical Gait Data Processing and the Assessment of GaitAbnormalities within a Neurorehabilitation Program

Callum Hsiaoclhxiao@gmail.com

University of Amsterdam

ABSTRACTGait analysis(GA) is a powerful tool for analysing locomotion biome-chanics, and it is particularly useful in the context of rehabilitationfor determining appropriate treatments and to evaluate the effective-ness of interventions. This study approaches clinical gait analysiswithin the context of a neurorehabilitation program using datacollected form a rehabilitation treadmill. A streamlined approach toassess gait abnormalities for the purpose of clinical evaluation wasdeveloped and validated. Using an automated rule based methodand vertical ground reaction force data, raw gait information issegmented and processed into gait cycles for feature extraction.In order to objectify clinical impression and document changes ingait pattern, four normalcy index for overall gait, hip joints, kneejoints and trunk flexion-extension were obtained using a methodbased on principal component analysis. The results demonstratea potential for the normalcy index to be used to quantify abnor-mal gait based on its degree of deviation from normality, and maybe adapted within future research as a robust metric to evaluaterehabilitation progress and aid with prognosis modeling.

KEYWORDSGait Analysis, Clinical Assessment, Normalcy Index, Kinematics,Biomechanics, Neurorehabilitation, Acquired Brian Injury

1 INTRODUCTIONGait is one of the most complex motor skills in humans, relying onthe coordination of multiple systems, including the central nervoussystem, peripheral nervous system and the musculoskeletal systemin order to produce a stable gait and consistent walking pattern[21][28]. Acquired brain injury (ABI), one of the most commoncause of death or disability among youths and young adults, canresult from events with an external or internal cause and lead tovarious functional impairments among which gait dysfunction iscommon [33][12]. Since impairments that contribute to gait disor-ders can vary considerably in its severity and type, the way eachindividual responds to therapy depends on a variety of variables[45]. As a result, gait training is often a significant focus of neu-rorehabilitation programs for patients with severe acquired braininjury [47].

Gait analysis is the systematic study and analysis of human loco-motion, and is often carried out in clinical practices to identify gaitimpairments [6]. It is a powerful assessment tool for surgical deci-sion making, post operative follow up, and obtaining informationthat can help establish and evaluate the level of functional limita-tion of gait due to pathology [8] [20] [6]. Traditionally, gait analysisis performed by therapists using a qualitative approach based ondirect visual observations [30]. However, while observational gaitanalysis plays an important role in clinical decision making and

evaluation, such approach can be also be time consuming and non-objective [2]. As such, following the advancement of biotechnology,increased importance have been placed on quantitative gait analy-sis. Through utilising joint angle kinematics and kinetics, groundreaction force and dynamic electromyography measurements thatcan be collected using modern technology, these automatic systemsconstitute an objective technique that is often more precise andcost effective [30][11][15]. Additionally, through the employment ofmodern data science methods, manipulation and insight extractionfrom large and more complex datasets can also be accomplished,further advancing the study of the biomechanics of human gait[14].

This project is proposed by the Daan Theeuwes Center (DTC)for Intensive Neurorehabilitation in Woerden, which specialises inproviding intensive and prolonged multidisciplinary treatments toyoung people with severe acquired brain injury. The quality careprogram concerned aims at improving the care offered based onstructured clinical data collection including a comprehensive profileof demographic and clinical characteristics, as well as prospectiverehabilitation outcome measurements [36]. For a preliminary anal-ysis, the targeted data for exploration is the gait measurementscollected using the machines newly introduced to the clinic aspart of the physical rehabilitation therapy. Specifically, the inter-pretation and analysis of the data collected by the rehabilitationtreadmill WalkerView 3.0 from TechnoBody Italy is of interest tothe therapists at the DTC.

The broad goal of this assignment is to explore the availabledata, and evaluate the extent to which it can be of value to thetreatment at the Daan Theeuwes Centre. This project thereforeaims to develop a processing pipeline to streamline the assessmentprocess of affected gait in the future and inform clinical decisionmaking and direct intervention programs [46]. The work shouldset a foundation for the systematic data collection that will aidfuture research for the development of prediction models that canhelp with rehabilitation prognosis. With this aim, this project willattempt to answer the following research question: How can thestructured data collected by the WalkerView 3.0 treadmill machine beused to aid the assessment of abnormal gait within a neurorehabilita-tion program through the use of quantitative analysis? To answer theresearch question, the main problem is divided into the followingsub-questions, characterised by the process of clinical gait dataanalysis:

• How can gait cycle extraction for gait analysis using kineticand kinematic gait data collected from the WalkerView beautomated?

• What relevant gait features can be obtained from the Walk-erView data and how can it be interpreted for clinical assess-ment through quantifiable measures?

• To what extent can the extracted gait features and summarymeasures help assess common pathological gait patterns inpatients with acquired brain injury and characterise abnor-mal gait in the evaluation of rehabilitative treatment pro-gresses? What value can it add to the overall rehabilitationprogram at the DTC?

This paper is divided into 7 sections. Following the above intro-duction of the research context and project aims, section 2 and 3will address the relevant background knowledge regarding clinicalgait analysis and existing research within the field. Section 4 willthen explain the method of data processing and analysis employedfor this project. In section 5, the results of the quantitative dataanalysis as well as the outcomes of said experiments will be pre-sented. The final sections will then discuss the obtained results andassess its relevance in the wider context of the intensive care pro-gram at the Daan Theeuwes Center. Addtionally, the added valueof integrating the WalkerView 3.0 as part of the neurorehabilitationtreatment program will be discussed, followed by an acknowledge-ment of the limitations of this study and a conclusion providingrecommendations for further research.

2 BACKGROUNDThis section provides the background knowledge regarding ac-quired brain injuries and the fundamentals of clinical gait analysisin which the analytical study is based upon.

2.1 Acquired Brain InjuriesAcquired brain injury (ABI) is any injury to the brain that occurspostnatal, and is not hereditary, congenital or degenerative [33].The resulting brain injuries can of traumatic or non-traumaticorigin, and the subsequent impairment could be either temporaryor permanent [41] [32]. The neurological consequences of an ABIcan severely affect the cognitive function of the victim throughcompromising the physical integrity or function of one or moreareas of the brain and lead to partial or total disability [19]. Inaddition to causing harmful clinical, social and economic effects, theinjured individual’s functional and psychosocial recovery prospectscan also be heavily affected [19].

2.2 Motion Analysis and BiomechanicsBiomechanics is the application of Newtonian mechanics to thestudy of the neuromuscular system [40]. Most commonly used inorthopaedics and the characterisation of function and dysfunctionof the muscular skeletal system, its principals are fundamental tothe study of human gait [40].

2.2.1 Gait Cycles, Gait Phases and Gait Events. Human gait com-prises a periodic phenomenon in which motions in both legs pro-duces a series of repetitive patterns called gait cycles that allowsan individual to move from one point to another [26]. The gaitcycle is defined as the period from one event of one foot to thefollowing occurrence of the same event with the same foot, gener-ally designated by the initial contact (also known as heel-contact,heel-strike or rearfoot strike), in which a foot contacts the groundheel-first during the foot strike phase of a walking cycle [16]. Thegait cycle consists of one stride length further divided into a stance

and swing phase, which is a period of weight-bearing and intervalof self-advancement respectively [16]. Each phase takes up approx-imately 60% and 40% of the gait cycle respectively and are classifiedby a cut-off gait event toe-off (also known as foot off), such thatthe interval between the toe-off point and heel-strike for a footrepresents the swing phase while the stance phase occurs betweenheel-strike and toe-off [26]. The two phases can then further be di-vided into seven sub-phases, including: Load Response, Mid Stance,Terminal Stance, Pre-Swing, Initial Swing, Mid-Swing and TerminalSwing [34] [37]. The beginning and end of each of these gait phasesare marked by specific gait events as seen in figure 1.

2.2.2 Spatio-Temporal Parameters of the Gait Cycle. Spatial andtemporal parameters are generally recognised as key metrics forcharacterising gait, wherein objective measures of such parametersallow for the characterisation of functional gait performance [5][9].The identification of specific gait events are required in order tocompute the required spatio-temporal parameters. At the very least,the initial contact and toe off moment must be identified in order toidentify the steps and strides and their parameters within a givenset of gait data.

Spatial gait parameters generally refer to step and stride length,which can be defined from the distance covered between two con-secutive Initial Contacts [5]. Meanwhile, the most common usedtemporal parameters for gait analysis include stride and step du-ration and cadence. Each gait cycle generally lasts the durationof around one second, also called stride time, and can further bedivided into double support stances and single support stances [40].Double support is the period of time when both feet are in contactwith the ground which occurs at the beginning and end of stancephase [16]. During double support, the weight is transferred fromone foot to the other [40]. Single support is the period of time whenonly one foot is in contact with the ground [16]. During a normalactive stride, it is equal to the swing phase of the other limb anddescribes when the center of mass of the body passes over the footin preparation for shifting to the other limb [40]. Furthermore, theduration of a temporal parameter can be characterised using unitin seconds, or as a percentage of a gait cycle.

2.2.3 Body Segments and Joint Kinematics. In order to analyse hu-man gait, the assumption is that each body segments involved inthe motion can be modeled as rigid bodies, where the position andmotion of the underlying skeleton can be tracked using a biome-chanical model in 3D space[40]. Most often used for such analysisis the Cartesian coordinate system, which consists of the anterior-posterior, medial-lateral, and longitudinal axis, corresponding to thefrontal, sagittal and tranverse planes respectively [1]. Furthermore,motion at the pelvis, hip, knee and joint ankles allow the analysisof temporal and stride events of the gait by recording the biome-chanical angular motion of the joints, including flexion-extension,internal-external rotation and abduction-adduction [20] [1]. Exam-ples of normative gait data can be seen in figure 5 in AppendixA.

2.2.4 Motion on the Sagittal Plane During the Gait Cycle. Due tothe constraint of the available data for this study, the focus wasplaced on hip and knee joint kinematic data on the sagittal plane.Critical events within a gait cycle including these parameters are

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Figure 1: Gait Cycle and Gait Events

described here and can be seen summarised in figure 6 in AppendixA.

Hip Flexion-Extension During Gait. Only a single arc of hip ex-tension and flexion occurs in each gait cycle, and during the initialcontact of a foot, the corresponding hip joint is generally in around30 degrees of flexion [31]. The hip joint motion then progressivelyextends into hyperextension, typically peaking at around 10 degreesclose to the toe-off event half way through the gait cycle, as flexionbegins again in terminal double stance and continues through mostof the swing phase, peaking at around 85% of the gait cycle wheremaximum hip flexion is achieved [31][27].

Knee Flexion-Extension During Gait. The knee joint goes throughtwo phases of flexion and extension in each gait cycle unlike thehip. During initial contact, the knee begins in full extension (orflexed roughly at 5 degrees), the rapidly flexes during the loadingresponse, reaching a first peak usually at around 15 degrees, thenprogressively extends to neutral until it begins to flex again with theonset of the double stance [31]. This flexing action then continuesinto the swing phase until it reaches a maximum flexion to roughly60 to 70 degrees at the beginning of mid-swing (73% of the gaitcycle) before extension is resumed until the next gait cycle [31].

2.2.5 Ground Reaction Forces and COG. Ground reaction force(GRF) is the only other force acting on the body while walkingaside from gravitational attraction if negating wind, and can bemeasured using by a pressure sensitive force plate on a flat surfacein which the gait measurement is conducted [40]. Output data of themeasurements can be processed into ground reaction force vectorcomponents that represent vertical load, shear loads, torque aboutthe vertical axis, and body center of pressure location [20]. Duringgait analysis, GRF can reveal minor changes of the gait pattern aswell as shifts in the centre of gravity, and can therefore validate thestate of disorder of a subject’s movement [48].

2.2.6 Kinetic Studies. With information on body segment motionsgathered from kinematic analysis, force data gathered from eithera force plate or force dynamometer can then be applied to it tocalculate the forces causing motion using Newton’s second law.Through application of equilibrium equations, the joint momentscan be computed and normalised and expressed as a percent ofbody weight times leg length. Furthermore, if the moment and jointare known, joint power can be calculated and incorporated into gait

analysis to provide insight into subtle functional musculoskeletaladaptations [20].

2.3 Gait analysisHuman gait analysis, being a critical component of the analysis ofindividual locomotion function, has seen extensive research and awide range of applications. The two main topics of general interestare gait identification and gait analysis for clinical applications.

2.3.1 Gait Identification. Gait identification refers to the recogni-tion of individuals based on their gait patterns and is generally usedin biometric identification, healthcare monitoring or surveillance[2][22]. Gait identification employs computer imaging technologyand are commonly based on deep learning models.

2.3.2 Clinical Gait Analysis. On the other hand, clinical analysisof gait aims to evaluate locomotion movements for the purpose ofgaining insight on human movement patterns corresponding todifferent gait pathology, which can support diagnoses and therapyconsiderations among other clinical applications [1][29]. The mainaim for clinical gait analysis, in addition to understanding generalhuman movement, is to assess and diagnose pathological gait. Thestudy of neuromuscular disorders and injuries is an area wheregait analysis offers great value, providing a non-invasive techniqueto evaluate the effects of neurological impairments on gait [13].Gait analysis consists of the collection of biomechanical data andthe subsequent processing and manipulation of it. 3 dimensionalgait analysis (3DGA) is the standard method for quantifying biome-chanical abnormalities during gait, and high level biomechanicaldata generated from 3D gait analysis have been shown to positivelyaffect clinical decision making and treatment outcomes in clinicalapplications such as the analysis of neurological conditions such ascerebral palsy, strike, and Parkinson’s disease [44].

The core component of most contemporary gait analysis is themeasurement of joint kinematics and kinetics, where temporaland spatial characteristics of the gait cycle is recorded [3] [35].Other measurements regularly made include electromyography,oxygen consumption and foot pressure [3]. Generally, gait dataappear as temporal waveforms representing specific joint measuresthroughout the gait cycle [13].

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3 RELATEDWORKGiven the background knowledge regarding gait biomechanics andthe function of clinical gait analysis, this section provides a briefoverview of the existing research concerning the use of data sciencetechniques to process and analyse clinical data, and how clinicalgait analysis are applied within rehabilitation programs.

3.1 Gait SegmentationAfter collecting gait data from recording different parts of the body,important gait features are usually extracted from individual gaitcycles for assessment and analysis. Generally, when analysing gait,the accurate and consistent segmentation of gait cycles from a dataset containing multiple cycles is essential, as is the detection of themain gait phases of stance and swing [24] [25].

3.1.1 Automated Gait Event Identification. In most studies, algo-rithmic methods to detect the gait events that mark the transitionbetween each gait phases were employed, and depending on theresearch, the aim may be to identify multiple gait sub-phases, orjust the two main ones stance and swing. However, since there areno standard methods to record gait data, it is likely every researchpresent in the available literature is working with a differentlystructured dataset. Nonetheless, gait segmentation are generallyperformed using similar strategies such as identifying peaks insignals that correspond to specific gait events such as initial con-tact and toe off, and often utilise ground force data, foot switchrecordings, kinematic data or a combination of two or more.

Ghoussayni et al. in [18] compared and validated a kinematicbased algorithm used in the detection of heel contact, heel rise,toe contact, and toe off. 3D marker coordinate data was filteredwherein marker velocities in the sagittal plane were then calculatedto determine the timing of the gait events using empirically setthresholds [18]. Likewise Khan and Badii [26] uses hip joints motiondata and a rule based algorithm to partition gait data into sevensubphases. Jiang et al. [24] proposed a method based on a peakdetection approach that can process different type of gait signalsand has a enhanced ability to segment gait cycles by eliminatingthe false peaks and interpolating the missing peaks, which wasdemonstrated to be effective through testing on data of patient’sdiagnosed with Parkinson’s disease.

3.2 Gait Features and Summary MeasuresAfter identifying gait cycles the next step of the analysis is to defineand extract parameters and features from the sample signal asdescriptors of discrete instants or events of the gait pattern [13].Feature extraction are generally approached locally or globally.The local method in the case of gait analysis consists of describingthe biomechanical data based on some specific points extractedfrom the acquired temporal waveform, including features such assummary statistics or parametrization involving measures on asingle biomechanical gait data.

3.2.1 Principal Component Analysis. The usefulness of principalcomponent analysis for gait feature extraction is often asserted dueto its effectiveness at dimensionality reduction and feasibility forvisual interpretation. While retaining the temporal characteristics,PCA can summarise information contained in the gait cycle and

reduce information contained in the biomechanical gait waveformsinto a small number of principal components [7]. Moreover, thefeatures elicited by PCA also often agree with the most clinicallyrelevant features [1]. As such, PCA is often used as a data reductiontool as well as preliminary step for further analysis to determinedifferences between patient and control groups, as demonstratedin Deluzio and Astephen’s study on knee osteoarthritis featuresidentification [13] and can also be seen used in various studiesof pathological gait or gait patterns such as analysis of gait varia-tions in stroke patients by Boudarham et al.[7] and classification ofneurological disorders of gait by Pradhan et al. [35].

3.2.2 Gait Summary Measures. While gait analysis provides aneffective tool for evaluating and quantifying the effects of a clinicalintervention or other treatment on a patient’s gait, objectively quan-tifying the degree to which a patient’s gait has improved followingan intervention remains difficult [8][39]. Over the last decade theneed for a concise index to summarise gait characteristics or tomeasure the ‘quality’ of a particular gait pattern has been raisedas typical 3D-GA evaluation produces a vast amount of data, anddespite its objectivity, it can be difficult to interpret [38]. Severalgait summary measures used in conjunction with 3DGA have beenproposed and researched for their possibility to objectify clinicalimpression and quantify the degree of gait deviation from normal,stratify the severity of pathology and document changes in gait overtime [8]. Notable ones include the gait normalcy index, hip flexorindex, gait deviation index and gait profile score among others [8].

4 METHODSFollowing the establishment of the context of this research, themethods employed to answer the research questions are explainedin this section. Specifically, this research aims to address the practi-cal needs at the Daan Theeuwes Centrum, and takes a focus on thedevelopment of a data processing pipeline and subsequent experi-ments.

4.1 Data DescriptionFirst, the data used in this study collected by the therapists at DTCusing the WalkerView 3.0 treadmill is described.

4.1.1 Sample population. The population of the admitted patientsat the DTC consists of young adults between the ages 16 and 35who have severe acquired brain injuries and seeks an intensiverehabilitation treatment. Around 46% of the patients were admittedas a result of traumatic brain injury, while 36% suffered a strokeand the remaining 18% from other non-traumatic causes1. Thepatient population that are participants for this preliminary studyconsists of 21 subjects. All are currently undergoing treatment andhave had their gait performance measured by the rehabilitationtreadmill 1 to 4 times over the span of a few months betweenFebruary to August 2020. Additionally, to provide healthy controlsamples for asymptomatic gait, 15 healthy subjects from the DTCwere measured to be used as preliminary reference samples for thisstudy.

1Information given by the DTC

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4.1.2 Instrumentation. The WalkerView 3.0 developed by Techno-body Italy was used to collect the gait data sample. The treadmillis equipped with a sensorized belt with eight load cells and allowsthe assessment of stance during ambulation and corrects dynamicparameters in real time [42]. The system is also equipped with a3D camera, which can receive instant and objective dynamic imageof the posture during training and display it in real time on the FullHD screen of the system. The 3D camera, using two optics, a classicone for HD 2D shootings and an infrared one allows for the analysisof a subject’s gait biomechanics as it can reliably capture any singleangular movement of the person walking on the treadmill [43].

4.1.3 Data Acquisition. Gait data were collected by having thesubjects complete a 2 minute standard test on the treadmill in whichthe WalkerView system will store into its internal operating system.Structured data in the form of a csv file are saved to the systemwhile it also generates a gait analysis report (see Appendix B). Thereport presents basic gait parameters such as the range of motion,maximum and minimum value of the trunk, hip and knee joints.Load symmetry as well as standard temporal parameters such asthe average step cycle time, step length, contact time and verticalCOG displacement are presented as well. The report also includesgraphs of the measured waveforms as well as the normalised andsegmented gait cycles, which is overlayed with a comparison of theentire detected biomechanical waveform and a normative rangereference (Appendix B fig 9).

At the current stage, however, the gait analysis report providedby the WalkerView is of ambiguous value to the therapists. Interestin the value of the quantitative data measured by the machine ispresent, however it is yet unclear what notable information can beobtained through the machine. With that, the following sectionsseek to explore and process the raw data stored in the csv files anddevelop a processing pipeline where specific analysis regardinggait abnormalities can be conducted through the use of quantitativemethods.

Examining the structured data stored in the csv file, it can beobserved that the WalkerView records measurements of groundforce reaction information, kinematic information of the trunk, hipand knees on the sagittal plane, the displacement of the centerof gravity and the left and right load. In figure 7 from AppendixB, visualisations of the temporal waveforms of the measured gaitdata of a random healthy control subject can be seen. Without anypreprocessing, the ground force reaction data collected by the 8 loadcells on the treadmill do not line up temporally with the kinematicdata due to different sampling frequencies.

4.2 Gait PartitionWith the time series data collected, gait cycles can be extractedthrough the identification of key gait events for the quantitativeanalysis of key gait parameters. For this preliminary analysis, arule based gait segmentation algorithm utilising hip joints angulardata was applied as hip flexion and extension are one of the mostimportant muscular activities in human locomotion [10]. It can beobserved in figure 7 that hip joint motion in the sagittal plane movesin a relative periodic fashion. It is implied that the maximum andminimum values for hip joint sagittal dimension angular motionindicate the flexion and extension extremes of the corresponding leg

respectively in a gait cycle [26]. Thus, a kinematic based algorithmthat exclusively processes hip joint data to partition gait phaseswas employed in order to obtain gait cycles for further analysis.

In addition to extracting gait cycles, identifying gait eventswithin each gait cycle is also an important procedurewhen analysinggait. For this, ground reaction force data was used in order to deter-mine the point in which each key gait event occurs, partitioning thegait cycles into the following gait events: Loading Response (LR),Mid Stance (MSt), Terminal Stance(TSt), Pre Swing(PSw), InitialSwing (ISw), Mid Swing (MSt), Terminal Swing(TSt).

A visual representation of the algorithm can be seen in figure 2.The output of each algorithm is a list of arrays of the starting indicesof every partitioned gait sub-phase. The results were entered intoanother function that obtains the start and end point of every gaitphase. First, gait segmentation was performed using smoothed hipkinematic data as input, after which each gait cycle was extendedor compressed in time to yield a normalised gait cycle of 101 datapoints and expressed as a function of a unit 100% cycle length,irrespective of the actual time for a stride for further analysis and theextraction of gait feature variables. The ground reaction force datafor each processed gait cycle were then used as input for the secondalgorithm in order to obtain the time each gait event occurs withina given gait cycle. Specifically, the left and right side average GRFobtained from averaging the GRF data from cell 1,3,5,7 and 2,4,6,8were used to determine the end point of the Mid Stance, TerminalStance and Initial Swing of each gait cycle, and information fromcell 3 and cell 4 in particular were used to find the point of Toe Off(end of Pre Swing), Loading Response and Mid Swing. The resultsobtained were compared to normative reference data in order todetermine the accuracy of the algorithms, additionally, abnormalinstances of identified gait events were counted.

Figure 2: Hip Joint Based Gait Segmentation Algorithm

4.3 Gait AnalysisFrom the gait cycles obtained as well as the indices of each gait sub-phases, key gait events were used to calculate spatio-temporal andsagittal kinematic variables for the gait cycles of interest. Clinicallyrelevant gait features that can be obtained through the WalkerViewdata can be seen summarised in table 6 in Appendix D. Summarystatistics were then generated for all the key gait variables and

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compared to the asymptomatic reference using a t test where ob-servations can be made to identify incidences of gait abnormalityfollowing acquired brain injuries. Additionally, individual resultsfor the patient sample were compared with the 95% confidenceintervals calculated for the corresponding variable for the healthycontrol sample, and values within ±2 standard deviations from themean were categorised as normal [46]. For the assessment of indi-vidual gait for clinical evaluation, detailed gait profiles that providemore accurate and objective results compared to observational gaitanalysis were generated, adding to the gait analysis report providedby WalkerView. This allows for the evaluation of specific gait char-acteristics as part of the assessment of clinical interventions, andcan help identify potential gait problems.

4.3.1 Normalcy Index. To accurately evaluate the extent of gaitdeviations from normal gait, or to assess the changes in a gaitresulting from a specific treatment, it is important to consider notonly how each feature of the gait pattern has changed but alsohow the relationship between the features changed. To evaluatewhether a specific gait variable is normal, abnormal, or improvedfollowing treatment, the natural correlation that exists betweengait variables must be determined [39]. For this reason multivariatestatistical techniques were used to develop a measure of how closelyan individual gait pattern approaches normal. This ‘closeness’ isreferred to as the normalcy index, which is the measure of thedistance between the set of discrete variables describing a patient’sgait pattern and the average of those variables in persons with nogait abnormalities [38].

Using a proposed method based on principal component analysis,the normalcy index quantifying gait deviations from the average ofnormal gait was obtained [38]. First, a set of𝑁 discrete gait variablesthat are correlated with the aspects of gait that the normalcy indexwill be describing were selected, and represented by 𝑥 𝑗 , 𝑗 = 1, 𝑁 .The process to obtain the normalcy index was as follows:

(1) Calculate the mean (` 𝑗 ) and standard deviation (𝜎 𝑗 ) of 𝑥 𝑗measured on M normal subjects.

(2) Standardise the data, defined by

𝑧 𝑗 = (𝑥 𝑗 − ` 𝑗 )/𝜎 𝑗(3) Calculate the covariance matrix (𝐶𝑖 𝑗 ) for the 𝑁 standardised

discrete variables. (𝑖 & 𝑗 = 1, 𝑁 )(4) Calculate the eigenvalue-eigenvector pairs (_𝑖 − 𝑒𝑖 ) for (𝐶𝑖 𝑗 ),

a subset of the eigenvectors will be used as basis vectors(5) To obtain the normalcy index for a subject, let it be be repre-

sented by the same N discrete variables 𝑥 𝑗 , 𝑗 = 1, 𝑁(6) Standardise 𝑥 𝑗 using the mean (` 𝑗 ) and standard deviation

(𝜎 𝑗 ) from the normal subjects

𝑧 𝑗 = (𝑥 𝑗 − ` 𝑗 )/𝜎 𝑗(7) Project 𝑧 𝑗 onto the basis vectors obtained from 𝑒𝑖 and stan-

dardise it by dividing it by the square root of _𝑖 to obtain𝑦𝑖

(8) Find the square of the Euclidean length of 𝑦𝑖 for a givensubject

𝑑 =

𝑁∑𝑖=1

𝑦2𝑖

The number 𝑑 represents the square of the distance of anindividual subject’s data from the normal mean in the newuncorrelated coordinate system and is defined as the nor-malcy index [38].

For this study, in addition to defining and exploring a general gaitnormalcy index based on gait variables that are generally consideredto be important and can be obtained through the available data,specific joint normalcy indices that aim to evaluate gait pathologiesat the joint level were also defined. Parameters were selected fromtheWalkerView data referencing previous studies, and the extractedgait features and joint parameters are can be seen summarised inin tables 7, 8, 9 and 10 in Appendix E [39][49].

To evaluate of the selected variables, the consequences of thechoices for each index was recalculated leaving out one variableeach time. Correlation coefficients between the index with all 𝑁parameters and index with one excluded parameter would indicatewhether a variable seem to dominate the final value [38]. As forthe validation of the obtained normalcy indices in clinical cases,the differences between the healthy subjects and patients werecompared.

4.4 Case StudiesTo assess how well the normalcy index can assist with the evalua-tion of a subject’s gait performance in a clinical setting, case studieswere conducted where six patients currently receiving treatmentat the DTC were selected based on data availability and separatedinto two groups based on observational judgment of the severityof their gait impairment. Gait analysis results from patient datacollected over the past few months were visualised and comparedto their clinical assessment data from the DTC.

5 RESULTS5.1 Automated Gait SegmentationExamples of gait segmentation results can be seen table 1 and figure10 in appendix C. In an average normative gait cycle, Load Responseusually ends at around 12% of the gait cycle, while Heel Rise occursat around 30%, terminating the Mid Stance phase. Furthermore,Terminal Stance ends at around 50% following Heel Rise, and ToeOff occurs at around 62% which marks the end of the stance phaseof a gait cycle. Toe Off is then followed by Initial Swing which endsat roughly 75% of the gait cycle, and lastly Mid Swing ends at 85%,leading to the final Terminal Swing phase [23].

For normal gait behaviour, gait event partition results as ob-tained from ground reaction force data appears to be reliable andconsistent, while more challenges are present in the partitioningof abnormal gait. This is especially the case for determining eachgait subphases due to the more irregular vertical ground reactionforce data (see fig 10 in appendix C), which can potentially affectthe extraction of certain gait features. Results in table 1 presentsthe mean value where each gait subphase ends within a gait cycle.Individual gait cycles for each subject where the results are deviatefrom the mean value by more than 2.5 standard deviations weredeemed as abnormal. From the results it can be seen that around9% of the gait cycles for each measured subjects have abnormalgait event occurrences as partitioned using the proposed algorithm,

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Table 1: Gait Phase Duration

Stance Phase Swing Phase

Loading Response Mid Stance(Heel Rise) Terminal Stance Pre Swing

(Toe Off) Initial Swing Mid Swing % Abnormal

Healthy L 12.05 ± 2.03 27.64 ± 1.36 45.25 ± 4.82 64.15 ± 1.64 77.53 ± 4.14 87.50 ± 5.92 9.45% ± 3.91%R 10.25 ± 2.26 24.15 ± 2.99 42.05 ± 5.57 60.93 ± 2.42 80.95 ± 2.90 89.39 ± 4.09 7.72% ± 4.40%

Patient L 15.05 ± 6.41 27.34 ± 5.43 43.67 ± 6.02 64.04 ± 6.10 77.67 ± 3.58 82.89 ± 4.49 9.64% ± 5.16%R 14.94 ± 6.07 26.26 ± 4.77 41.56 ± 5.72 63.02 ± 5.51 80.90 ± 3.32 88.95 ± 5.11 9.43% ± 5.09%

with patient subjects on average having more abnormal gait cyclescompared to healthy subjects.

5.2 Quantitative Gait AnalysisThe summary statistics of the healthy control group and the patientdata samples are outlined in table 2, while the joint kinematic datain the sagittal plane are summarised in in table 3. Moreover, thefrequency for the classification of abnormal gait variables that fallsoutside of 95% CI can be seen in table 4. As can be observed fromthe results, the classification of abnormality varies depending onthe variable. The patient sample was significantly different fromthe healthy control samples for 7 out out of 20 variables, and morethan a quarter of the samples was classified as abnormal (table 4).Notably, the spatio temporal variables analysed appear to deviatemore significantly from the healthy control values, and it can beobserved that the patient population generally adopted a slowerwalking speed as reflected in the decreased cadence and lowerstride time. Patients also tend to have a greater stance phase andsignificantly increased double support duration, with a notableportion of subjects having increased knee flexion at initial contactbut decreased knee flexion overall, especially for its range of motionduring the swing phase.

5.3 Normalcy IndexThe correlation between index calculated with all parameters andone excluded parameters can be seen in tables 7, 8, 9 and 10 inAppendix E alongwith the specific chosen variables for this analysis.According to the correlation coefficient calculated, it appears thatthe normalcy index is for themost part rather sensitive to the precisecomposition of gait variable combinations. The hip joint index andknee joint index shows a slight exception, with the correlationvalues being relatively closer to 0.90 compared to the general gaitnormalcy index and trunk index.

Next, in figure 3 and table 5 the normalcy index of healthy andpathological patient subject group are presented. The mean in-dex for the patients are notably higher than that of the healthycontrol group and showcases a statistically significant difference.This result shows that the proposed gait normalcy index and jointnormalcy index was able to distinguish between normal and abnor-mal gait based on the chosen parameters, where patients with amore dysfunctional gait obtaining a higher score in general, with afew notable outliers receiving a significantly higher score (see alsofigure 13 in Appendix E).

5.3.1 Case Studies. To evaluate the relevance and viability of thenormalcy index, a few case studies was conducted. Six patients were

Figure 3: Normalcy Index

chosen based on an observational assessment of their recorded gaitperformance from the archived WalkerView video data and dividedinto two groups. Group A were patients who have received a higherFunctional Ambulation Category (FAC) score during their initialreport and can perform a 2 minute test on WalkerView indepen-dently, while Group B were patients who have more severe gaitdysfunction and had to rely walking aid throughout their rehabil-itation. Each selected patients has at least three WalkerView testdata available, measured over the period of at least three and a halfmonths.

From information obtained from the earliest available measure-ment for the subjects, the normalcy index obtained from group Awere 0.84, 76.22 and 1.5 for overall gait; 16.46, 74.35, 7.77 for hip;13.25, 246.11, 76.46 for knee and 15.51, 21.38, 25.5 for trunk. Forgroup B the normalcy index values were 50.08, 15.54 and 138.84for overall gait; 154.28, 123.04, 228.39 for hip; 144.09, 468.66, 204.23for knee and finally, 348.16, 71.43 and 204.29 for trunk. Significantdifference is noticeable between the two groups, as patients withmore drastically abnormal gait analysis results receiving highernormalcy index scores as expected. For some subjects, however, itis possible to score low on the normalcy index, indicating a smalldeviation from an average normal gait pattern, while scoring highon the other indexes. Through observing the difference betweenhigher and lower gait index scores, it appears to mainly evaluatethe periodic consistency within the entire gait pattern.

Figure 4 is an example of the gait evaluation report for one of thecase studies. The report gives information on the normalcy index,and visualises the gait cycle for the hips, knees, trunk on the sagittalplane as well as the lateral COM displacement. A segment of theraw data is also visualised, with the normalcy index labeled on top

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Table 2: Spatio Temporal Variables

Patient (n = 21) Healthy (n=15)Gait Variable Mean ± SD Range Mean ± SD Range 𝑝

Cadence 108.09 ± 21.31 44 - 134 127.67 ± 12.02 98 - 156 0.003Stance Duration (s) 0.71 ± 0.25 0.48 - 1.67 0.56 ± 0.06 0.40 - 0.73 0.025Initial Double Support (s) 0.17 ± 0.08 0.06 - 0.42 0.10 ± 0.02 0.05 - 1.15 0.002Single Support (s) 0.70 ± 0.19 0.38 - 1.46 0.63 ± 0.11 0.39 - 0.82 0.175Terminal Double Support (s) 0.25 ± 0.18 0.02 - 0.88 0.17 ± 0.05 0.13 - 0.34 0.076Stride Time 1.12 ± 0.36 0.83 - 2.50 0.90 ± 0.10 0.66 - 1.15 0.031

Table 3: Kinematic Variables

Patient (n = 21) Healthy (n=15)Gait Variable Mean ± SD Range Mean ± SD Range 𝑝

Trunk Flexion (deg) 4.17 ± 1.46 2.62 - 8.28 3.40 ± 0.70 2.39 - 4.84 0.066Trunk Lateral Flexion (deg) 4.04 ± 1.76 1.87 - 8.55 2.97 ± 0.97 1.80 - 4.55 0.040Hip ROM (deg) 43.29 ± 7.28 30.06 - 54.52 46.09 ± 5.73 33.75 - 53.95 0.224Hip Flexion at Initial Contact (deg) 24.03 ± 5.20 11.58 - 31.59 26.16 ± 3.59 18.57 - 32.16 0.180Hip Flexion at Heel Rise (deg) 1.05 ± 3.91 -7.58 - 7.99 2.18 ± 3.17 -2.91 - 7.07 0.362Hip Peak Flexion (deg) 33.55 ± 7.62 21.77 - 55.21 32.23 ± 7.71 24.09 - 56.75 0.613Knee ROM during Stance (deg) 27.08 ± 7.07 13.60 - 39.32 29.39 ± 4.40 19.91 - 37.24 0.272Knee ROM during Swing (deg) 40.27 ± 9.39 22.83 - 55.14 48.83 ± 5.11 41.10 - 59.15 0.003Knee Flexion at Initial Contact (deg) 13.47 ± 6.11 5.10 - 22.87 9.59 ± 3.74 5.33 - 20.01 0.037Knee Flexion at Heel Rise (deg) 10.75 ± 5.71 2.76 - 18.96 11.74 ± 5.36 6.53 - 25.91 0.600Knee Flexion at Toe Off (deg) 30.94 ± 9.76 13.83 - 48.18 34.72 ± 6.54 21.04 - 45.62 0.201Knee Peak Flexion (deg) 63.64 ± 10.66 42.52 - 95.71 66.21 ± 6.65 58.36 - 77.88 0.415Time at Peak Knee Flexion (deg) 71.13 ± 5.52 52.23 - 78.65 73.19 ± 2.17 69.13 - 77.69 0.037Lateral COM Displacement (cm) 3.71 ± 1.16 1.47 - 5.59 4.35 ± 1.31 3.06 - 7.82 0.127

of each graph. Furthermore, the point for toe off, identifying thedivision between the stance and swing phase is also visualised onthe gait cycle visualisations. The shaded area represents the totalobserved gait activity while the lighter shaded area represents thegait information of the last measured data. This is to show potentialgait improvement in order to assist with rehabilitation evaluation.

The example given in figure 4 is the second measurement ofsubject 2 from Group A. According to the information in the data-base, this subject have suffered an ischemic stroke. Assessmentconsistent with the clinical data such as decreased knee flexion andabnormality in hip movement is reflected in the normalcy indexand the visualisations. Through the visualisation, improvementscan also be seen, particularly in regards to knee movement, eventhough the degree of abnormality is still high.

6 DISCUSSION6.1 Gait Analysis using the WalkerViewGait analysis here is done using raw data from the Walkerview 3.0fromTechnoBody, whichwas originally developed to be usedwithinathletic rehabilitation programs and not aimed for clinical use. Asa result, it was unclear to the clinicians how the data providedby the machine can be interpreted for clinical assessment. Theaim was thus to develop a processing pipeline to explore the datacollected and investigate its potential added value for rehabilitation

assessment. The first step is to figure out how to partition the rawgait data into individual gait cycles.

6.1.1 Automated Gait Segmentation. When analysing gait, firstand foremost the data needs to be processed, and one of the keystep is to extract gait cycles and identify key gait events. Usingan automated method to extract gait cycles is the advantage ofdigital analysis. Here, the method employed identifies each gaitcycle using the periodic hip joint kinematic data through findingthe negative peaks of each hip joint in order to obtain the startand end point of a each gait cycle for the opposite leg. The resultsof the gait cycle extraction is consistent and accurate, indicatinga robust algorithm, although there were still potential for errorswhen dealing with highly irregular or abnormal gait data, and inthis case some of that error was corrected manually. Furthermore,since ground reaction force (GRF) data is also available, it wasinvestigated in using GRF data to more accurately partition gait.However, the technically correct way to interpret the GRF datarecorded in the raw WalkerView file remained unclear. Throughvisualising the data and comparing it to available references online,notable patterns were identified. It was determined that data fromcell 3 and 4 in particular could be used for identifying the keygait phases loading response and toe off for each gait cycle, asthe resulting value obtained through this gait partitioning methodyielded results that accurately reflected normative reference data.

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Table 4: Incidence of Gait Variable Abnormalities

Gait Variable Decreased (%) Normal (%) Increased (%)Cadence 6 (28.6) 15 (71.4) 0 (0.0)Stance Duration (s) 0 (0.0) 13 (61.9) 8 (38.1)Initial Double Support (s) 1 (4.76) 6 (28.6) 15 (71.4)Single Support (s) 1 (4.76) 18 (85.7) 2 (9.52)Terminal Double Support (s) 0 (0.0) 14 (66.7) 7 (33.3)Stride Time 0 (0.0) 17 (80.1) 4 (19.0)Trunk Flexion (deg) 0 (0.0) 17 (80.1) 4 (19.0)Trunk Lateral Flexion (deg) 0 (0.0) 16 (76.2) 5 (23.8)Hip ROM (deg) 4 (19.0) 17 (80.1) 0 (0.0)Hip Flexion at Initial Contact (deg) 5 (23.8) 16 (76.2) 0 (0.0)Hip Flexion at Heel Rise (deg) 6 (28.6) 13 (61.9) 2 (9.52)Hip Peak Flexion (deg) 0 (0.0) 19 (90.5) 2 (9.52)Knee ROM during Stance (deg) 6 (28.6) 11 (52.4) 4 (19.0)Knee ROM during Swing (deg) 10 (47.6) 11 (52.4) 0 (0.0)Knee Flexion at Initial Contact (deg) 2 (9.52) 14 (66.7) 5 (23.8)Knee Flexion at Heel Rise (deg) 2 (9.52) 18 (85.7) 1 (4.76)Knee Flexion at Toe Off (deg) 7 (33.3) 12 (57.1) 2 (9.52)Knee Peak Flexion (deg) 5 (23.8) 15 (71.4) 1 (4.76)Time at Peak Knee Flexion (deg) 6 (28.6) 13 (61.9) 2 (9.52)Lateral COM Displacement (cm) 1 (4.76) 20 (95.2) 0 (0.0)

Table 5: Normalcy Index Results

Patients Healthy SubjectNormalcy Index Mean Range Mean Range

Gait L 43.375 ± 156.733 0.169 - 720.227 0.933 ± 1.427 0.013 - 5.554R 20.815 0.278 - 66.428 1.167 0.004 - 4.061

Hip Joint L 89.557 ± 290.436 4.212 - 1327.536 6.533 ± 3.103 2.623 - 12.172R 67.049 5.985 - 231.266 2.154 2.449 - 10.003

Knee Joint L 126.318 ± 220.339 6.849 - 960.117 7.467 ± 2.376 3.389 - 11.979R 161.549 7.072 - 743.182 2.648 4.328 - 12.150

Trunk Movement 96.618 ± 115.230 4.401 - 469.462 6.533 ± 1.784 2.753 - 9.380

Thus, the averaged result of the left and right side GRF data andinformation from load cell 3 and cell 4 in particularly were chosenfor the analysis done in this research. Nonetheless, as can be seenin the results in table 1, incidences of irregularity in GRF data isa regular occurrence and was not handled in the proposed gaitpartitioning algorithm. Potentially in the future, a more robust gaitsegmentation method that incorporates both kinematic and kineticdata could improve the performance. Furthermore, adding ankledata, which was not collected for this research could potentiallyhelp to more accurately determine gait events.

6.1.2 Quantitative Gait Analysis and the Normalcy Index. It is ev-ident that gait is a highly subjective characteristic, which provesa high challenge when evaluating incidences of gait abnormality.Generally, the approach is to compare a patient’s gait to healthygait samples, which is a similar method the observational methodis based on.

It is found through the quantitative analysis of extracted gaitvariables here that people with acquired brain injuries have multi

joint gait abnormalities [46]. To address the extent of deviationfrom normality and account for the relationship between correlatedgait variables, normalcy indexes (NI) were defined and assessed.The goal of NI is to use one single index to determine how much asubject’s gait deviate from the average normal gait to account forthe subjectivity. The measurement is as a result, general in nature,but proves to be a useful tool in objectivity quantifying overallchanges in gait, especially in the case for specific joints.

In the case studies performed, the normalcy index proved tobe robust enough to clearly indicate incidences of pathologicalwalking patterns, and was able to identify specific leg and jointsthat suffered injury, and can also assist in the assessment of rehabprogress through the assessing the change in the normalcy index.The hip and knee index appears to be the most informative, beingable to accurately identify hip and knee pathology as well as subtleperformance issues, as is the case with subject 3 of group A wherethe reported right knee injury is reflected in the normalcy score ofthe right knee. Furthermore for the case of Group B, the patientshave more severe gait dysfunction and had to rely on harnesses to

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Figure 4: Gait Evaluation Report Selection: Case Study Group A Subject 2 data 2

perform their walking trial. This may have affected the measure-ment collected, nonetheless the gait analysis also proves to be ableto provide accurate assessment, particularly in identifying the mainside of the patient where there are issues.

In evaluating extent of improvement, the normalcy index seemto be most useful, as it is shown that NI can help quantify changesin gait in order to evaluate the effects of intervention and treat-ment. However, it should be noted that while NI can help evaluatechanges in gait resulting from treatment, it does not necessarilyindicate whether there has been any improvement or degradationof function. This is because there is no established relationshipbetween normalcy of gait pattern and function, since abnormalitiesin gait pattern can represent functional coping mechanisms [17].Therefore for more complete assessment of the normalcy indexnaturally will have to be used in conjunction with other outcomemeasures, and other parts of the neurorehabilitation treatment (seeAppendix F).

6.2 LimitationsThe sample size available for this study is limited. A key challengein gait analysis is that there is no standardised gait reference data.Therefore, for each gait analysis research, unique samples needto be collected. As such, due to the small sample size of healthycontrol subjects with that were collected arbitrarily and do not fit astrict, scientifically controlled demographic, the reliability of thereference has to be doubted. Also, main conventional gait data thatwould be implemented into quantitative analysis was either notproperly utilised (GRF), not present in the raw data (movementspeed and distance), not collected (ankle joint data), or not analysedby the WalkerView (kinematic data on the other planes).

7 CONCLUSIONThe central question in clinical gait analysis is to understand whata "correct" or "healthy" way of walking is, and to answer the ques-tions: Why do we walk the way we do and why don’t the patients

do so? Has a patient improved? And how so? In a clinical setting,especially one for rehabilitation, the main purpose of gait analysisis to 1) distinguish diagnosis between disease entities, 2) determineseverity of disease or injury, 3) assess, evaluate and select treatmentoptions, 4) predict prognosis following intervention or absence ofintervention [4]. Quantitative gait analysis can be useful to helpidentify specific treatment option for specific pathological gait,and make up for the lack of objectivity of observational analyses.Nonetheless, multitudes of challenges are still present, for the lackof standardised normative gait data to review as well as the extentof variability within human gait itself. It should be noted that manydisagreements within the medical community still exists when itcomes to understanding what affects pathological gait and whichabnormal gait characteristics are important to address when decid-ing on intervention. Thus, when approaching clinical gait analysis,this should be taken into account.

This exploratory study successfully implemented a gait process-ing pipeline including gait segmentation algorithms and gait featureextraction methods in order to produce clinical analysis profiles formeasured subject at the DTC. By comparing gait parameters of thepatients to healthy sample subjects, patterns for general abnormali-ties can be observed and evaluated. Additionally, using multivariatestatistical methods to summarise 3DGA information into normalcyindex proved to be able to provide clinical insight into gait abnor-malities. While limitations such as a lack of coordinated sample datacollection strategy and a shortage of reference data is present, theresearch design and processing pipeline proposed in this researchis nonetheless valuable.

This research offers therapists an approach to interpret rawWalkerView data and provides a foundation for future study suchas making prediction models for rehabilitation prognosis. The nor-malcy index studied here indicates potential, however it should stillbe researched and refined in order to determine the most reliableand accurate gait variables for evaluation. Furthermore, a long termdata collection strategy should be employed, and should aim tofully utilise all the analytical capacity offered by the WalkerView.

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ACKNOWLEDGEMENTI would like to give special thanks to Ruud van der Veen and dr.Erik Bekkers for providing primary supervision during this projectperiod and accommodated me throughout the process. I wouldalso like to thank UvA, UMC and the Daan Theeuwes Centrumalong with dr. Frank Nack, dr. Marsh Königs, study advisors CeciliaSigvardsdotter, Sophie Tjebbes and fellow student Wietske Dotinga,Sean Hladkyj for their support during the project. Lastly I extendmy gratitude to everyone who believed in me and provided mewith assurance throughout, this has been quite a year, quite a yearindeed.

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APPENDIXA GAIT ANALYSIS REFERENCE

Figure 5: Normative Gait Data

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Figure 6: Gait Cycle and Critical Gait Events Summary

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B WALKERVIEW DATA REPORTING

Figure 7: walkerview raw data top to bottom: (1,2) GRF, (3) Kinematics, (4,5) Hips, (6,7) Knees, (8,9) Trunk, (10) Load, (11) COG

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Figure 8: WalkerView Gait Analysis Report Overview Front Page

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Figure 9: WalkerView Gait Analysis Report Overview Gait Cycle Visualisation

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C GAIT SEGMENTATION

Figure 10: Gait Segmentation Results Visualised

D GAIT VARIABLES

Table 6: Gait Variables and Descriptors

Parameters Unit DescriptionSpatioTemporal

Step Time sec average step durationStride Time sec average stride durationCadence steps/min average steps taken per minute

Stance Phase duration sec & (%GC) percentage of stance phase per each gait cycleSwing Phase duration sec & (%GC) percentage of swing phase per each gait cycle

Single Support sec & (%GC) Time duration in GC when single foot is bearing body weightDouble Support sec & (%GC) Time duration in GC when both feet and bearing body weight

Initial Double Support sec duration of initial double support phaseTerminal Double Support sec duration of terminal double support phase

Kinematic

Trunk Flexion deg range between maximum and minimum value in the gait cycleTrunk Lateral Flexion deg range between maximum and minimum value in the gait cycleHip & Knee ROM deg range of motion of hip and knee joints during each gait phaseHip Extension deg peak value at terminal stanceKnee Flexion IC deg angle at initial contactKnee Flexion MS deg angle at mid stance

Knee Flexion Swing deg angle at toe offVertical Displacement COG cm range between maximum and minimum in the gait cycle

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E NORMALCY INDEX

Table 7: Gait Variables for normalcy index

Gait Parameter Gait Phase Unit Correlation1 Stance Time Stance %GC 0.5762 Cadence Stance/Swing step/min 0.4973 Hip Peak Extension Stance/Swing deg 0.0464 Hip Peak Flexion Swing deg 0.2515 Hip ROM Stance/Swing deg 0.9386 Knee Flexion Initial Contact deg 0.4107 Knee Flexion Toe Off deg 0.9178 Time Peak Knee Flexion Swing %GC -0.1769 Knee ROM Stance/Swing deg -0.02810 COM Displacement Stance/Swing cm -0.209

Table 8: Hip variables for normalcy index

Gait Parameter Gait Phase Unit Correlation1 Hip Peak Extension Stance/Swing deg 0.8802 Hip Peak Flexion Swing deg 0.8323 Hip ROM Stance deg 0.9484 Hip ROM Swing deg 0.9055 Hip std ROM Stance/Swing deg 0.8866 Hip std Minima Stance/Swing deg 0.6697 Hip std Maxima Stance/Swing deg 0.9318 Hip Flexion Initial Contact deg 0.612

Table 9: Knee Variables for normalcy index

Gait Parameter Gait Phase Unit Correlation1 Knee Peak Flexion Swing deg 0.8442 Knee ROM Swing deg 0.8453 Knee std ROM Stance/Swing deg 0.9544 Knee std Minima Stance/Swing deg 0.9315 Knee std Maxima Stance/Swing deg 0.9496 Knee Flexion Initial Contact deg 0.9347 Knee Peak Flexion Time Swing deg 0.8658 Mean Flexion Extension Velocity Loading Response deg/s 0.9629 Minimum Flexion Extension Velocity Terminal Stance deg/s 0.942

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Table 10: Trunk Variables for normalcy index

Gait Parameter Gait Phase Unit Correlation1 Trunk ROM Swing deg 0.2022 Trunk ROM Stance deg 0.2543 Trunk ROM Stance/Swing deg 0.3154 Trunk std Minima Stance/Swing deg 0.1775 Trunk std Maxima Stance/Swing deg 0.0676 Trunk Lateral ROM Swing deg 0.4457 Trunk Lateral ROM Stance deg 0.3698 Trunk Lateral ROM Stance/Swing deg 0.423

Figure 11: Normalcy Index Scores

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F MEASUREMENTS TAKEN IN NEUROREHABILITATION PROGRAM AT THE DTC

Figure 12: Physical, Occupational, Speech, Neuropsychological Therapy Measurements

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Figure 13: Medical Rehabilitation and Social Rehabilitation Measurements

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