Movement & Sport Sciences - Science & Motricité 2017, 98, 77–88© ACAPS, EDP Sciences, 2017https://doi.org/10.1051/sm/2017016

Science MotricitéMovement Sport Sciences

Available online at:www.mov-sport-sciences.org

ARTICLE

Identifying and understanding gait deviations: critical reviewand perspectivesStéphane Armand1,*, Florent Moissenet2, Geraldo de Coulon3, and Alice Bonnefoy-Mazure1

1 Willy Taillard Laboratory of Kinesiology, Geneva University Hospitals and Geneva University, Geneva, Switzerland2 Centre National de Rééducation Fonctionnelle et de Réadaptation –Rehazenter, Laboratoire d’Analyse du Mouvement et de laPosture (LAMP), Luxembourg, Luxembourg

3 Pediatric Orthopaedic Service, Department of Child and Adolescent, Geneva University Hospitals and Geneva University,Geneva, Switzerland

Received 10 February 2017, Accepted 17 July 2017

*Correspon

Abstract -- Walking is paramount in the activities of the daily life of the human being and can be altered inneuro-musculoskeletal disorders. It is therefore essential to identify and better understand abnormal gaitpatterns linked to these disorders and to treat them. Clinical gait analysis (CGA) is a medical examination thatobjectively identifies gait deviations. CGA includes video recordings, spatiotemporal parameters, kinematics,kinetics and electromyography of a patient’s gait.The objective of this article is to critically review conventional methods to identify gait deviations andmethodsused to improve understanding of gait deviations.After addressing conventional CGA, this article provides new avenues to better identify gait deviations based onexisting literature. Different approaches to improve the understanding of gait deviations are presented. Theyare based on pathological models, experimental procedures, numerical simulations, anthropomorphic robotsand animal models.

Keywords: gait, clinical gait analysis, kinematics, kinetics, EMG, gait deviations

Résumé -- Identification et compréhension des troubles de la marche : revue critique etperspectives. La marche, primordiale dans les activités de la vie quotidienne de l’être humain, peut êtrealtérée par des troubles neuro-musculo-squelettiques. Il est donc essentiel d’identifier et comprendre ces troublespour les traiter. L’analyse quantifiée de la marche (AQM) est l’examen médical qui permet d’identifierobjectivement les troubles de la marche. L’AQM inclut généralement l’enregistrement de séquences vidéo, desparamètres spatio-temporels, de la cinématique, de la cinétique et de l’électromyographie de la marche d’unpatient.L’objectif de cet article est de réaliser une revue critique des méthodes conventionnelles pour identifier lestroubles de la marche et des méthodes utilisées pour améliorer la compréhension des troubles de la marche.Après avoir décrit l’AQMconventionnelle, cet article donne de nouvelles pistes pourmieux identifier les troublesde la marche en se basant sur la littérature existante. Les différentes approches pour améliorer la compréhensiondes troubles de la marche sont présentées. Elles sont basées sur des modèles pathologiques, des procéduresexpérimentales, des simulations numériques, des robots anthropomorphes, des modèles animaux.

Mots clés : marche, analyse quantifiée de la marche, cinématique, cinétique, EMG, troubles de la marche

1 Introduction

Walking is often considered as the most importantactivity in daily living (Chiou, Burnett, Chiou II, &Burnett, 1985). The ability to move without pain, fatigue

ding author: Stephane.armand@hcuge.ch

or any gait deviation is closely related to the quality of life(Cuomo et al., 2007; van Schie, 2008). Numerousconditions [e.g., aging (Aboutorabi, Arazpour, & Bahra-mizadeh, 2016)] or pathologies [e.g., neurodegenerativediseases (Morris, Lord, Bunce, Burn, & Rochester, 2016)]cause specific gait deviations (Hausdorff & Alexander,2005). Consequently, preservation of gait with minimal

78 S. Armand et al.: Mov Sport Sci/Sci Mot 2017, 98, 77–88

deviations is the goal of many treatments. In order tooptimise these treatments, the understanding of underly-ing mechanisms and underlying pathologies of gaitdeviations is of utmost importance. Due to the complexityof gait, and pathological gait in particular, instrumentedgait analysis is generally used to quantify and understandthe deficits of an individual and is fully integrated in theclinical decision-making of patients with complex gaitdisorders due to neuro-orthopaedic pathologies (Davids,Ounpuu, DeLuca, & Davis, 2004; Gough & Shortland,2008; Wren, Gorton, Õunpuu, & Tucker, 2011). Instru-mented gait analysis is a measurement of an individual’smovement pattern during walking. The core of thisanalysis consists in measuring joint kinematics andkinetics in three dimensions, as well as electrical muscularactivity. These measures are commonly based on thetrajectory of passive reflective cutaneous markers placedon different body segments, forces and moments capturedby forceplates embedded in the walkway, as well as surfaceelectromyography (EMG). In clinical settings, instru-mented gait analysis is commonly called clinical gaitanalysis (CGA). CGA provides an objective record thatquantifies the magnitude of deviations between pathologi-cal and normal gait (Baker, 2006). In addition to CGA,complex characteristics of locomotion are assessedthrough clinical testing composed of a set of assessmentsthat evaluates the functionality of neuromuscular andmusculoskeletal structures (Gage, Schwartz, Koop, &Novacheck, 2009). These assessments permit the identifi-cation and characterisation of clinical impairments (motorimpairments and bone deformities).

The management of gait deviations is basicallyperformed in three key steps (Moissenet & Armand,2015). The first step is to identify gait deviations. Itinvolves precise and individual measurements of bio-mechanical and neurophysiological parameters duringwalking in a standardized protocol. In this article,conventional methods and potential alternatives will bepresented. The second step is to understand gaitdeviations. To this aim, it is necessary to link theidentified gait deviations with their possible causes. It iscertainly themore difficult task because links between gaitdeviations and clinical impairments are poorly established(Desloovere et al., 2006; Sangeux & Armand, 2015). Thisarticle will present conventional interpretations of gaitdeviations and discuss new methods to enhance theknowledge of interpreting these deviations. The final stepis to choose the most appropriate treatment based ontreatment effects on identified gait deviations andunderlying causes, and dependent on many other factorssuch as the experience of the medical team, the medicalimaging data available, the patient expectations, theequipment available, the potential risks, the rehabilitationtime, the costs, etc. The choice of treatment will not bediscussed in this article.

Therefore, the aims of this article are to perform acritical review of conventional methods used to identifygait deviations, and potential newmethods to improve theunderstanding of gait deviations.

2 Identify gait deviations

The identification of gait deviations is usually based onvisual inspection and curves based on different types ofdata for a specific patient as compared to a control group ofasymptomatic persons.

In this article, we will not discuss in detail the models,tools and methodologies used to obtain data, but ratherfocus onmethods to represent data in order to identify gaitdeviations. However, it is noteworthy that data qualityinfluences the identification of gait deviations.

2.1 Conventional CGA2.1.1 Protocol

CGAare performed in dedicated laboratories equippedwith synchronised motion analysis systems (Fig. 1) com-monly including:

– video cameras (frontal and sagittal views) to recordpatients during walking;

–

an optoelectronic system with several cameras tocapture the 3D trajectories of passive reflective cutane-ous markers fixed on the body;

–

forceplates embedded in the walkway to capture the 3Dground reaction force and moment;

–

a surface EMG system to capture electrical muscularactivity.

In some laboratories, pressure distribution of the footand oxygen consumption during walking are also assessed.

Firstly, a clinical testing is generally performed on thelower-limbs commonly including the assessment of:

– muscle strength using ordinal scales [e.g., MedicalResearch Council scale (Florence et al., 1992)]. Alterna-tively, isokinetic dynamometer or a hand-held dyna-mometer can be used (Fosang & Baker, 2006);

–

selective motor control using the selective controlassessment of the lower extremity (SCALE) (Fowler,Staudt, Greenberg, & Oppenheim, 2009);

–

spasticity evaluated through the use of ordinal scales[e.g., Modified Ashworth Scale (Bohannon & Smith,1987), Modified Tardieu Scale (Ben-Shabat et al.,2013)];

–

passive range of motion with a manual goniometer toevaluate soft tissue contractures (Viehweger, Berard,Berruyer, & Simeoni, 2007).

These assessments permit the identification andcharacterisation of clinical impairments (see Baker(2013) for more details on the clinical testing associatedwith CGA).

Secondly, the patients are tested barefoot and dressedin swimming suit or underwear. They are equipped:

– with passive reflective cutaneous markers on anatomicaland technical landmarks according to the biomechanicalmodel used by the laboratory [e.g., Plug-in-Gait (DavisIII et al., 1991), Leardini (Leardini et al., 2007),International Society of Biomechanics (ISB) (Wu,Cavanag, & Cavanagh, 1995)];

Forceplate

Optoelectronic system Video

camera

Electromyography system

Fig. 1. Illustration of a common clinical gait analysis equipment.

S. Armand et al.: Mov Sport Sci/Sci Mot 2017, 98, 77–88 79

–

with surface EMG electrodes on the recorded surfacemuscles of the lower limbs (e.g.,Gluteii, Rectus femoris,Vastii, Hamstrings, Tibialis Anterior, Peroneii, Gastro-cnemii, Soleus) according to the SENIAM (Europeaninitiative called surface electromyography for non-invasive assessment of muscles) recommendations (Her-mens, Freriks, Disselhorst-Klug, & Rau, 2000).

Thirdly, the patients walk at self-selected speed on a10-meter walkway for several trials (commonly between3 and 20 trials depending on the capacity of the patientand the objective of the assessment). The examination isfinished when the evaluator decides that a sufficientnumber of gait cycles have been recorded and that therecorded data is representative of the patient’s gaitpattern.

2.1.2 Different types of data in CGA and representation

CGA acquires numerous data commonly representedin a clinical report as illustrated in Figure 2:

– gait scores resume the overall quality of gait based on thekinematics of the lower limb. The most commonly usedscores are the gait profile score (Baker et al., 2009) andthe gait deviation index (Schwartz&Rozumalski, 2008);

–

spatiotemporal parameters are linked with the gait cyclecharacteristics. They are computed from foot-off andfoot strike events, and markers trajectories of the foot.Themost commonly used parameters are walking speed,cadence, stride/step length, step width, and duration ofthe different phases (i.e., stance, swing, single support,double support);

–

kinematic data represent measures of the angularvariation of segments or joints in the sagittal, frontaland transverse planes. They are computed based on thebiomechanical model chosen with commercial software(e.g., Nexus-Vicon, Visual 3D-CMotion) or home-madecomputation (e.g., Matlab-The Mathworks). Since

recently, it is also possible to use open source develop-ments independent from the system of motion capture[e.g., OpenMA (Barre, Leboeuf, & Armand, 2016)];

–

kinetic data correspond to the forces required to achievethe observed motion. The main variables are groundreaction forces, joint moments and joint powers. Theircomputation is also linked to the biomechanical model.Estimation of inertial parameters for each segment isneeded for these computations;

–

electromyography data show the timing of muscleactivation. If the signal is normalised in amplitude,the level of activation can be compared between days forthe same subject or between subjects (Burden, Trew, &Baltzopoulos, 2003). This normalisation can be done forexample by measuring the level of activation during amaximal or a sub-maximal voluntary contraction(Ghazwan, Forrest, Holt, & Whatling, 2017).

Time series data (i.e., kinematics, kinetics, EMG) aregenerally time-normalised according to the gait cycle. Leftand right sides are aligned with the foot strike to facilitatethe comparison (Baker, 2013).

In addition to discrete and time series data, the reportcontains synchronised video and 3D animations that allowfor a general overview of the patient’s gait.

2.2 Potential alternatives

Numerous potential alternatives to the conventionalCGA exist to represent or/and to compute variables basedon the raw data. The scientific literature is abundant butonly few alternatives are used in clinical practice that referto the choice of:

– the protocol; – the biomechanical model; – the representative data; – the representative cycles; – the method used to normalise data;

A C

D

E

F

B

G

Fig. 2. Set of data reported for a common clinical gait analysis.

80 S. Armand et al.: Mov Sport Sci/Sci Mot 2017, 98, 77–88

–

the normative data; – the method to compare data with normative data.

We will illustrate these 7 sub-sections with someexamples linked with kinematic data.

2.2.1 Choice of the protocol

Numerous protocols exist to improve themeasurementand identification of gait deviations. Existing protocols,models, equipments used, can be modified and adapteddepending on the particularity of gait deviations tohighlight. Concerning the choice of tasks in the protocol,it remains debatable if walking barefoot at self-selectedspeed on a 10-meter walkway is the best way to identifygait deviations. For example, walking slower or faster canhelp to identify gait deviations (Gross et al., 2013). VanCampenhout et al. also used fast walking to highlight the

influence of spasticity on gait (Van Campenhout et al.,2014). Moreover, specific tasks for several gait deviationscan be performed. For instance, idiopathic toe-walkerscould be asked to walk as best as possible with a heel-toepattern in order to identify that part of gait deviationsthat can be voluntarily corrected (Westberry, Davids,Davis, & de Morais Filho, 2008). Accordingly, we can askpatients to walk with internal and external foot rotation tohighlight rotation problems, to perform a 6-minutewalking test to assess walking endurance (Pohl et al.,2002; Beriault et al., 2009), to walk on incline surface(Agrawal et al., 2015), to turn (Orendurff et al., 2006), etc.Numerous walking tasks are possible according to theimpairment(s) to highlight.

In addition to that specific gait tasks, functionaltests can be performed to highlight a specific deficit. Forinstance, the single leg stance can be used to assess

Fig. 3. Stick figure of a patient for one gait cycle in the sagittalplane.

S. Armand et al.: Mov Sport Sci/Sci Mot 2017, 98, 77–88 81

hip abductor muscle strength (Allison et al., 2016), thesit-to-stand movement to highlight quadriceps weakness(Bryanton & Bilodeau, 2017), or the weight-bearinglunge test to highlight contracture of the triceps(Powden, Hoch, & Hoch, 2015).

Concerning the equipment to assess kinematics, therehas been an important development of inertial sensors(inertial measurement unit – IMU) within the last decade.IMUs are generally composed of a 3D gyroscope, a 3Daccelerometer, and a 3Dmagnetometer. Several synchron-ised IMUs can be fixed on the bodies and used to computekinematics. The main advantages are that no specificlaboratory settings are needed to perform the measure-ments and that IMUs are cheaper than optoelectronicsystems. However, it remains difficult to accuratelycompute 3D joint kinematics due to issues related toferromagnetic disturbances, to sensor-to-segment align-ment and to the proprietary sensor fusion algorithm’saccuracy (Picerno, 2017). Nevertheless, IMUs open a newarea to explore gait analysis during daily life to detect theactivity (e.g., standing, walking, running), to assess thequantity (e.g. duration, intensity) and to characteriseactivity (e.g., spatiotemporal parameters, kinematics).These data will give new insights on actual performance(i.e., what the patient does in daily life) which iscomplementary to capacity (i.e., what the patient cando) measured in the laboratory.

2.2.2 Choice of the biomechanical model

Several biomechanical models exist to computekinematics, [e.g., convention gait model (Davis III et al.,1991), Leardini (Leardini et al., 2007), ISB (Wu et al.,1995)] and numerousmethods are available to improve theaccuracy of the models [e.g., calibration (Stagni, Fantozzi,& Cappello, 2009)], optimisation [e.g., SCoRE, SARA(Taylor et al., 2010)], geometric correction based onanatomic assumptions (Baker, Finney, & Orr, 1999),multibody kinematics optimisation (Andersen, Dam-sgaard, & Rasmussen, 2009; Reinbolt et al., 2005).Another very promising method is to fusion gait analysisdata with medical imaging data to precisely locate bonystructures (e.g., iliac spines, hip joint centres) in order toimprove the accuracy of the computations (Assi et al.,2013). The choice of the model and the associatedcorrective methods is critical to reduce the effect of softtissue artefacts (Leardini, Chiari, Della Croce, & Cap-pozzo, 2005) (i.e., the motion of skin, fat and musclesgliding on the underlying bone) and potential offsets onthe computed kinematics and kinetics that may lead tomisinterpretation of gait deviations.

2.2.3 Choice of the representative variables

Despite the fact that numerous variables are generallycomputed, the same variables are often considered sincethe first CGA in 1970s (Sutherland, 2002) (Fig. 2). Basedon the same raw data, numerous alternatives have beenproposed but are not often used in CGA. For example, a

simple alternative for the assessment of lower limbkinematics is to represent elevation angles, i.e., the anglebetween the segment and the vertical (Borghese, Bianchi,& Lacquaniti, 1996). Elevation angles permit to evaluatethe coordination between segments using planar covari-ance (Leurs et al., 2012). Relative phases can also be usedto quantify inter- and intra-limb coordination (Lamb &Stöckl, 2014). Another important parameter oftenmissingin CGA reports is the foot-ground angle that presents theorientation of the foot in relation to the ground. Markertrajectories are also rarely used in CGA [except torepresent a stick figure (Fig. 3)]. However, based onmarker trajectories, it is possible to highlight specific gaitdeviations such as circumduction of the hip [e.g., by usingLissajous figures (Itoh et al., 2012) (Fig. 4)] or such asminimum foot clearance (minimum vertical distancebetween the lowest point of the foot of the swing leg andthe walking surface during the swing phase of the gaitcycle) that is a gait variable linked to the mechanism of atrip (Barrett, Mills, & Begg, 2010).

2.2.4 Choice of the representative cycles

Firstly, all the cycles in CGA are reported for selectedvariables to assess the reliability of gait. Secondly,representative cycles can be reported that serve as a basisfor the interpretation of gait deviations. Severalmethods ofselection have been proposed. Some laboratories use acentral tendency as themeanwith plus-minus one standarddeviationwhereas other laboratories select a representativegait cycle. This selection can be performed by the expert orby different algorithms (Sangeux &Polak, 2015; Schweizeret al., 2012). For example, Sangeux and Polak, proposed amethodbased on the functionalmediandistance depth thatpermits to select a representative cycle and detect outliersfor gait kinematics (Sangeux & Polak, 2015).

82 S. Armand et al.: Mov Sport Sci/Sci Mot 2017, 98, 77–88

2.2.5 Choice of the method to normalise data

-200 -100 0 100 200

Medio-lateral displacement (mm)

-200

-100

0

100

200

300

400

Ant

ero-

post

erio

r di

spla

cem

ent (

mm

)

Fig. 4. Lissajous figures to represent circumduction of the lowerlimb. Lissajous figures at the horizontal plane of the ankle(malleolus) marker in a healthy side (left-red) and in hemipareticside (right-blue) a patient with right hemiplegia. The linedenotes the stance phase, and the dotted line denotes the swingphase. The stars indicate initial contact. This figure is based onthe Itoh et al.’s study (Itoh et al., 2012).

Generally, variables in CGA are time-normalisedaccording to the gait cycle. As the relative duration ofthe sub-phases of the gait cycle (i.e., stance, swing, doublesupport, single support) are depending on walking speed,it needs to be questioned if the normalisation according tothe full gait cycle (i.e., stride time) is the best way to reportthe data (Forner-Cordero, Koopman, & Van Der Helm,2006). Moreover, for the same patient the comparisonbetween conditions (e.g., walking barefoot, walking withshoes, walking with external help), between differentsessions of CGA for the same patient and comparisonbetween patient’s datawith normal data, is affected by thenormalisation especially if there is a difference in walkingspeed between these different conditions. Indeed, differentwalking speeds will lead to different gait outcomes(Kirtley, Whittle, & Jefferson, 1985; Schwartz, Rozumal-ski, & Trost, 2008).

Several other methods of normalisation have beenproposed. For example, it is possible to normalise dataaccording to sub-phases of the gait cycle (i.e., stance,swing, double support, single support) or to a specific pointof interest. This simple technique, named piecewisetemporal alignment techniques, outperforms other com-monly used alignment methods (e.g., normalisation topercent gait cycle, dynamic time warping, derivativedynamic time warping) in typical biomechanical andclinical alignment tasks (Helwig, Hong, Hsiao-Wecksler, &Polk, 2011).

In addition to time-normalisation, gait data can benormalised in amplitude to minimise the unavoidableinter-individual differences or to make the interpretationeasier. This can be achieved by reporting dimensionlessparameters (Hof, 1996). Kinetics (i.e., moments, powers)can then be normalised by the mass and/or the height ofthe subjects. Muscle activity can be expressed inpercentage of a maximal voluntary contraction orsubmaximal tasks. Muscle length can be expressed inpercentage of maximal muscle length (need musculoskele-tal modelling). However, it must be noted that Chia &Sangeux recently reported undesirable properties of thedimensionless normalisation and proposed alternativesdepending the objectives (Chia & Sangeux, 2017).

2.2.6 Choice of the normative data

Generally, laboratories establish normative datarepositories for children or adults at a spontaneouswalking speed (Pinzone, Schwartz, Thomason, & Baker,2014). Themean and standard deviation of the establisheddata repositories are used as normative data. Although,gait parameters are influenced by many variables such aswalking speed (Schwartz et al., 2008), age (Sutherland,Olshen, Cooper, & Woo, 1980), gender (Cho, Park, &Kwon, 2004); these influences are poorly considered whenlaboratories compare the patient’s gait data with norma-tive data. To better identify gait deviations, it may beinteresting to discriminate gait deviations due to depen-dent variables (i.e., walking speed, age, gender) and gaitdeviations due to impairments. To this aim, it may beinteresting to establish normative datawith the possibilityto match gait data of a specific patient for velocity, age,and gender. Moreover, it should be noted that it exists aninter-individual gait variability and that walking is notsymmetric (Allard, Leteneur, Watelain, & Begon, 2017).Gait signals can be considered as the signature of a personand can be used for automatic recognition of a person(Foster, Nixon, & Prügel-Bennett, 2003). Therefore, wecan consider that it does not exist one normal gait butnumerous normal gaits. Normal gait variability can bereduced by controlling velocity, age, and gender but notremoved.

2.2.7 Choice of the method to compare data withnormative data

During the CGA’s interpretation, the patient’s dataare commonly visually compared to normative data.However, based on conventional statistics, Schwartz et al.proposed to compare data with point-by-point assess-ments of significance (Schwartz, Trost, & Wervey, 2004).More recently, Pataky adapted a method called statisticalparametric mapping (SPM) to the time-series recordedduring CGA (Pataky, 2010). It is noteworthy that thesemethods are affected by the normalisation of data and thatdifferences can be due to the fact that temporal alignmentbetween the patient’s data and the normative data is notrespected (e.g., when the normalisation is performed on

Fig. 5. Example of common gait deviations identified in cerebral palsy by Sangeux & Armand (Sangeux & Armand, 2015).

S. Armand et al.: Mov Sport Sci/Sci Mot 2017, 98, 77–88 83

the full gait cycle). Numerous statistical methods havebeen adapted to time-series gait data. For instance,bootstrap method can be used to compute confidencebands (Duhamel et al., 2004; Lenhoff, Santner, Santner,Otis, Peterson, 1999). Moreover, significant statisticaldifference does not necessarily imply clinical significance.Therefore, for each parameter and each population, it maybe necessary to estimate the minimal clinical importantdifference (MCID) (i.e., a clinical level of significance) asproposed by Baker et al. for the gait profile score (GPS) incerebral palsy children (Baker et al., 2012).

Illustration of significant or relevant gait deviationsshould be done by highlighting a part of the patient’scurves and by representing a normal gait curve area as it isillustrated on Figure 5.

3 Understanding gait deviations

Understanding gait deviations is not obvious due thecomplexity of the human neuro-musculoskeletal systemand the dynamic nature of walking. A large variety of gaitdeviations can be observed in patients (Fig. 5) (Sangeux&Armand, 2015). Establishing the relationship betweenmotor impairments and gait deviations is the primaryclinical objective to understand gait deviations and toestablish treatment strategies. To achieve this goal, it isnecessary to distinguish between the impact of theimpairments associated with the pathology, and thecompensatory strategies developed by the patient(Romkes & Brunner, 2007). Only impairments are the

target of the treatments (Davids, 2006) since compensa-tory strategies should disappear once these impairmentshave been addressed (Schmid, Schweizer, Romkes,Lorenzetti, & Brunner, 2013).

3.1 Conventional way to understand CGA

Despite the strong evidence of an existing relationshipbetween clinical impairments and gait deviations, theimpact of each impairment on gait deviations is rarelyelucidated (Boudarham et al., 2014). Indeed, severalstudies showed poor correlations between clinical param-eters (related to impairments) and CGAvariables (relatedto gait deviations) (Aktas, Aiona, & Orendurff, 2000;Desloovere et al., 2006; Domagalska et al., 2013;McMulkin, Gulliford, Williamson, & Ferguson, 2000).More recently, Bonnefoy-Mazure et al. (2016) reportedthat the CGA time-series cannot be predicted by a linearcombination of clinical parameters.

3.2 Methods to bring new knowledge

Different approaches have already been proposed inthe literature to establish links between clinical impair-ments (including motor impairments and bony deformi-ties) and gait deviations. We have identified five mainapproaches:

– pathological models; – experimental procedures; – numerical simulations;

84 S. Armand et al.: Mov Sport Sci/Sci Mot 2017, 98, 77–88

–

anthropomorphic robots; – animal models.

3.2.1 Pathological models

Pathologic models aim to further understand gaitdeviations by comparing patients with one single, well-identified impairment and healthy subjects. Using thisapproach, gait compensatory strategies have been found inmuscle weakness (Perry & Mulroy, 1995; Simonsen,Moesby, Hansen, Comins, & Alkjaer, 2010), spasticity(Bar-On et al., 2014) and soft tissue contractures (West-berry et al., 2008) for several muscles. This traditionalapproach can be used in a clinical setting. However,patients rarely have isolated motor impairments and theinterpretation becomes very hazardous when there iscomplex interaction between several impairments. Thisissue may be addressed through the use of advancedstatistical or machine learning methods based on a largepatient database. Machine learning approach has beenpromoted to contest the view of linear relationshipbetween motor impairments and gait deviations. Sagawaet al.managed to extract rules explaining the degree of gaitseverity [i.e., using the gait deviation index (Schwartz &Rozumalski, 2008)] with motor impairments (Sagawaet al., 2013). Even though the results were useful inhighlighting the motor impairments that lead to severegait deviations, the findings did not link specific gaitdeviations to clinical impairments. Sangeux et al. man-aged to associate gait classifications in CP children withsome impairments based on the plantar-flexor-kneeextension couple (Sangeux, Rodda, & Graham, 2015).However, scientific evidence establishing a direct relation-ship between motor impairments and gait deviations isstill missing. In the future, machine-learning methods willcertainly help to identify these complex relationships.

3.2.2 Experimental procedures

Experimental procedures that assess the impact ofvoluntary or induced perturbations in healthy subjects arehereafter related to the term “emulation”. This approachhas been employed to investigate gait deviations provokedby experimentally induced muscle weakness (Boudarhamet al., 2014; Kendall et al., 2013), due to spasticity(Okumura et al., 2015) and soft tissue contractures (Attiaset al., 2016; Harato et al., 2008; Matjačić&Olen�sek, 2007;Matjačić, Olen�sek, & Bajd, 2006; Olen�sek, Matjačic, &Bajd, 2005), but also due to voluntary modification ofwalking such as toe-walking (Riley & Kerrigan, 2001;Romkes & Brunner, 2007) or crouch gait (van der Krogt,Doorenbosch, & Harlaar, 2007), experimentally inducedpain (Henriksen, Rosager, Aaboe, & Bliddal, 2011;Henriksen et al., 2007), and modification of visionconditions (Logan et al., 2010). This approach mayfacilitate the understanding of the phenomenon andmay thus have a significant clinical impact. Indeed, onekey advantage is to isolate a particular motor impairmentin order to account for its consequences on gait and

therefore to establish a link with gait deviations.Moreover, motor impairment emulations could be mergedto investigate their combined effect on gait. However, therelated procedures can present ethical issues (e.g., inducedpain, induced weakness) limiting their practical applica-tion; and/or the difficulty to test all potential isolated andcombinations of motor impairments. Nevertheless, thisapproach represents a unique and versatile way ofexploring gait deviations related to motor impairments.

3.2.3 Numerical simulations

Over the past 20 years, numerical simulation andmodelling of the musculoskeletal system have becomeincreasingly valuable tools to study normal (Pandy &Andriacchi, 2010) and pathological gait (van der Krogt,Delp, & Schwartz, 2012), or to understand the relatedunderlying mechanisms (Goldberg & Neptune, 2007;Pandy & Andriacchi, 2010; Ren, Jones, & Howard,2007). This approach has been recently used to simulatemuscle weakness (Afschrift, De Groote, De Schutter, &Jonkers, 2014; Ardestani & Moazen, 2016; Goldberg &Neptune, 2007; Knarr, Reisman, Binder-Macleod, &Higginson, 2013; Thompson, Chaudhari, Schmitt, Best,& Siston, 2013; Valente, Taddei, & Jonkers, 2013; Van derKrogt et al., 2012), spasticity (Jansen et al., 2014; van derKrogt, Bar-On, Kindt, Desloovere, & Harlaar, 2016), andsoft tissue contractures (Van der Krogt et al., 2016). Anadvantage of such an approach relies on its potential tomanage “what-if” scenarios, e.g., to plan the impacts of anintervention (Arnold, Ward, Lieber, & Delp, 2010).Although several issues related to the validation of modelscurrently limit their direct clinical applications (Lund, deZee, Andersen, Andersen, & Rasmussen, 2012), thisapproach already supplies educative tools facilitatingthe understanding of complex phenomena. Indeed, the useof generic or personalised models represents in itself aversatile tool allowing assessment of many impairmentconditions. The studies of Jansen et al. (2014) and van derKrogt et al. (2012) perfectly illustrate how a phenomenonrelated to a specific pathological condition can beinvestigated by altering several parameters of the model.However, the way that these parameters must be alteredto mimic pathological conditions is not obvious andrequires experimental inputs. Such a procedure hasalready been applied by several authors and yieldedencouraging results. For example, in a recent study, vander Krogt et al. (2016) fed their simulations byexperimental data obtained from measurements per-formed on CP children to tune the model parametersand to reproduce spasticity and soft tissue contractureduring specific clinical testing manipulations.

3.2.4 Anthropomorphic robots

Indeed, theuseof robotswith two legs reflectinghuman-like gait offers the opportunity to test different walkingconditions that can provide a better understanding ofhuman locomotion (Ephanov & Hurmuzlu, 2002; Renjew-

S. Armand et al.: Mov Sport Sci/Sci Mot 2017, 98, 77–88 85

ski & Seyfarth, 2012). This approach has the potential todefine a unique experimental framework to reproduce gaitdeviations andhas beenused to explore the functionof heel-off in walking (Renjewski & Seyfarth, 2012) and theinfluence of leg spasticity (Kikuchi, Oda, & Furusho, 2009;Kikuchi, Oda, Yamaguchi, & Furusho, 2010). However, upto now, no bipedal robot has had the capacity ofreproducing the normal human gait in terms of bothbalance and kinematics. Therefore, the practical applica-tion of this approach is currently limited (Okumura et al.,2015), although the development of passive compliantrobots, such as the robot COMAN (Li, Tsagarakis, &Caldwell, 2013), shows promising results through the use offlexible joints to produce more human-like movements.

3.2.5 Animal models

Animal models are employed extensively in the studyof human diseases such as neurodegenerative conditions(e.g., Parkinson, Alzheimer) [Blesa, Trigo-Damas, delRey, & Obeso, 2017], neurologic lesions (e.g., spinal cordinjury) [Haefeli, Huie, Morioka, & Ferguson, 2017], orosteoarthritis (Kuyinu, Narayanan, Nair, & Laurencin,2016). Animal models offer the opportunity to manipulatediseases and to investigate new treatments (Conn, 2013).For instance, gait disorders have been studied with mousemodels for experimental parkinsonism and the effect ongait from different treatments have been tested (Broomet al., 2017). Gait disorders in primates have beenalleviated by a brain-spinal interface (Capogrosso et al.,2016). However, incompatibility of quadruped animalmodels and biped human gait remains a major barrier fortranslation (Broom et al., 2017).

4 Conclusions

Identifying and understanding gait deviations areprimordial to treat patients and to improve their quality oflife. Even if clinical gait analysis provides relevantinformation to address gait deviations, we are convincedthat this examination can be improved by a betteridentification and a better understanding of gait devia-tions. Concerning the identification, numerous innovativemethods exist to enhance measurements, to adaptprotocols, to compute new variables or to reportdifferently existing variables. Concerning the understand-ing of gait deviations, the integration of the results fromfundamental studies on the control and the biomechanicsof locomotion with the results of applied studieshighlighting the influence of specific impairments on gaitis needed. Once the identification and understanding ofgait deviations will be improved, treatment strategies forpatients with gait deviations will also improve.

ReferencesAboutorabi, A., Arazpour, M., & Bahramizadeh, M. (2016). The

effect of aging on gait parameters in able-bodied oldersubjects: a literature review.Aging Clinical and ExperimentalResearch, 28/3, 393–405.

Afschrift, M., De Groote, F., De Schutter, J., & Jonkers, I.(2014). The effect of muscle weakness on the capability gapduring gross motor function: a simulation study supportingdesign criteria for exoskeletons of the lower limb. BiomedicalEngineering Online, 13/1, 111.

Agrawal, V., Gailey, R.S., Gaunaurd, I.A., O’Toole, C.,Finnieston, A., & Tolchin, R. (2015). Comparison of fourdifferent categories of prosthetic feet during ramp ambulationin unilateral transtibial amputees. Prosthetics and OrthoticsInternational, 39/5, 380–389.

Aktas, S., Aiona, M.D., & Orendurff, M. (2000). Evaluation ofrotational gait abnormality in the patients cerebral palsy.Journal of Pediatric Orthopedics, 20/2, 217–220.

Allard, P., Leteneur, S., Watelain, E., & Begon, M. (2017).Urban legends in gait analysis.Movement & Sport Sciences –Science & Motricité. In press.

Allison, K., Bennell, K.L., Grimaldi, A., Vicenzino, B., Wrigley,T.V., & Hodges, P.W. (2016). Single leg stance control inindividuals with symptomatic gluteal tendinopathy.Gait andPosture, 49, 108–113.

Andersen, M.S., Damsgaard, M., & Rasmussen, J. (2009).Kinematic analysis of over-determinate biomechanical sys-tems. Computer Methods in Biomechanics and BiomedicalEngineering, 12/4, 371–384.

Ardestani, M.M., & Moazen, M. (2016). How human gaitresponds to muscle impairment in total knee arthroplastypatients: muscular compensations and articular perturba-tions. Journal of Biomechanics, 49/9, 1620–1633.

Arnold, E.M., Ward, S.R., Lieber, R.L., & Delp, S.L. (2010). Amodel of the lower limb for analysis of human movement.Annals of Biomedical Engineering, 38/2, 269–279.

Assi, A., Chaibi, Y., Presedo, A., Dubousset, J., Ghanem, I., &Skalli, W. (2013). Three-dimensional reconstructions forasymptomatic and cerebralpalsy children’s lower limbs usinga biplanar X-ray system: a feasibility study. EuropeanJournal of Radiology, 82/12, 2359–2364.

Attias, M., Chevalley, O., Bonnefoy-Mazure, A., De Coulon, G.,Cheze, L., &Armand, S. (2016). Effects of contracture on gaitkinematics: a systematic review. Clinical Biomechanics, 33,103–110.

Baker, R. (2006). Gait analysis methods in rehabilitation.Journal of NeuroEngineering and Rehabilitation, 3, 4.

Baker, R. (2013).Measuring walking: a handbook of clinical gaitanalysis. London: Mac Keith Press.

Baker, R., Finney, L., & Orr, J. (1999). A new approach todetermine the hip rotation profile from clinical gait analysisdata. Human Movement Science, 18/5, 655–667.

Baker, R., McGinley, J.L., Schwartz, M.H., Beynon, S.,Rozumalski, A., Graham, H.K., & Tirosh, O. (2009). Thegait profile score and movement analysis profile. Gait andPosture, 30/3, 265–269.

Baker, R., McGinley, J.L., Schwartz, M., Thomason, P., Rodda,J., & Graham, H.K. (2012). The minimal clinically importantdifference for the gait profile score. Gait and Posture, 35/4,612–615.

Bar-On, L., Molenaers, G., Aertbeliën, E., Monari, D., Feys, H.,& Desloovere, K. (2014). The relation between spasticity andmuscle behavior during the swing phase of gait in childrenwith cerebral palsy. Research in Developmental Disabilities,35/12, 3354–3364.

Barre, A., Leboeuf, F., & Armand, S. (2016). OpenMA: a newopen source library for working and altering the conventionalgait model. Gait and Posture, 49/Supplement 2, 260.

Barrett, R.S., Mills, P.M., & Begg, R.K. (2010). A systematicreview of the effect of ageing and falls history on minimumfoot clearance characteristics during level walking. Gait andPosture, 32/4, 429–435.

86 S. Armand et al.: Mov Sport Sci/Sci Mot 2017, 98, 77–88

Ben-Shabat, E., Palit, M., Fini, N. A., Brooks, C.T., Winter, A.,& Holland, A.E. (2013). Intra-and interrater reliability of theModified Tardieu Scale for the assessment of lower limbspasticity in adults with neurologic injuries. Archives ofPhysical Medicine and Rehabilitation, 94/12, 2494–2501.

Beriault, K., Carpentier, A.C., Gagnon, C., Ménard, J.,Baillargeon, J.P., Ardilouze, J.L., & Langlois, M.F. (2009).Reproducibility of the 6-minute walk test in obese adults.International Journal of Sports Medicine, 30/10, 725–727.

Blesa, J., Trigo-Damas, I., del Rey, N.L.-G., & Obeso, J.A.(2017). The use of nonhuman primate models to understandprocesses in Parkinson’s disease. Journal of Neural Trans-mission. Available from https://link.springer.com/article/10.1007%2Fs00702-017-1715-x#citeas.

Bohannon, R.W., & Smith, M.B. (1987). Interrater reliability ofa Modified Ashworth Scale of muscle spasticity. PhysicalTherapy, 67/2, 206–207.

Bonnefoy-Mazure, A., Sagawa Jr. Y., Pomero, V., Lascombes,P., DeCoulon, G., Armand, S. (2016). Are clinical parameterssufficient to model gait patterns in patients with cerebralpalsy using a multilinear approach? Computer Methods inBiomechanics and Biomedical Engineering, 19/7, 800–806.

Borghese, N.A., Bianchi, L., & Lacquaniti, F. (1996). Kinematicdeterminants of human locomotion. The Journal of Physiolo-gy, 494/3, 863–879.

Boudarham, J., Roche, N., Pradon, D., Delouf, E., Bensmail, D., &Zory, R. (2014). Effects of quadriceps muscle fatigue on stiff-knee gait in patients with hemiparesis.PLoSONE, 9/4, e94138.

Broom, L., Ellison, B. A., Worley, A., Wagenaar, L., Sörberg, E.,Ashton, C., Bennett, D.A., Buchman, A.S., Saper, C.B., Shih,L.C., Hausdorff, J.M., & VanderHorst, V.G. (2017). Atranslational approach to capture gait signatures of neurologi-cal disorders inmiceandhumans.ScientificReports, 7/1, 3225.

Bryanton, M., & Bilodeau, M. (2017). The role of thighmuscularefforts in limiting sit-to-stand capacity in healthy young andolder adults. Aging Clinical and Experimental Research, 1–9.Available from https://link.springer.com/article/10.1007%2Fs40520-016-0702-7#citeas.

Burden, A.M., Trew, M., & Baltzopoulos, V. (2003). Normal-isation of gait EMGs: a re-examination. Journal of Electro-myography and Kinesiology, 13/6, 519–532.

Capogrosso, M., Milekovic, T., Borton, D.,Wagner, F., Moraud,E.M., Mignardot, J.-B., Buse, N., Gandar, J., Barraud, Q.,Xing, D., Rey, E., Duis, S., Jianzhong, Y., Ko,W.K.D., Li, Q.,Detemple, P., Denison, T.,Micera, S., Bezard, E., Bloch, J., &Courtine, G. (2016). A brain-spine interface alleviating gaitdeficits after spinal cord injury in primates. Nature, 539/7628, 284–288.

Chia, K., & Sangeux, M. (2017). Undesirable properties of thedimensionless normalisation for spatio-temporal variables.Gait & Posture, 55/April, 157–161.

Chiou, I.I., Burnett, C.N., Chiou II, & Burnett, C.N. (1985).Values of activities of daily living. A survey of stroke patientsand their home therapists. Physical Therapy, 65/6, 901–906.

Cho, S.H., Park, J.M., & Kwon, O.Y. (2004). Gender differencesin three dimensional gait analysis data from 98healthyKorean adults. Clinical Biomechanics, 19/2, 145–152.

Conn, P.M. (2013). Animal models for the study of humandisease. Amsterdam and Boston: Academic Press.

Cuomo, A.V, Gamradt, S.C., Kim, C.O., Pirpiris, M., Gates, P.E., McCarthy, J.J., & Otsuka, N.Y. (2007). Health-relatedquality of life outcomes improve after multilevel surgery inambulatory children with cerebral palsy. Journal of PediatricOrthopedics, 27/6, 653–657.

Davids, J.R. (2006).Quantitative gait analysis in the treatment ofchildren with cerebral palsy. Journal of Pediatric Orthopae-dics, 26/4, 557–559.

Davids, J.R., Ounpuu, S., DeLuca, P.A, & Davis, R.B. (2004).Optimization of walking ability of children with cerebralpalsy. Instructional Course Lectures, 53, 511–522.

Davis III, R.B., Tyburski, D., Ounpuu, S., Gage, J.R. (1991). Agait analysis data collection and reduction technique.HumanMovement Science, 10/5, 575–587.

Desloovere, K., Molenaers, G., Feys, H., Huenaerts, C.,Callewaert, B., & Van de Walle, P. (2006). Do dynamicand static clinical measurements correlate with gait analysisparameters in children with cerebral palsy?Gait and Posture,24/3, 302–313.

Domagalska, M., Szopa, A., Syczewska, M., Pietraszek, S.,Kidon, Z., & Onik, G. (2013). The relationship betweenclinical measurements and gait analysis data in children withcerebral palsy. Gait and Posture, 38/4, 1038–1043.

Duhamel, A., Bourriez, J.L., Devos, P., Krystkowiak, P., Destée,A., Derambure, P., &Defebvre, L. (2004). Statistical tools forclinical gait analysis. Gait and Posture, 20/2, 204–212.

Ephanov, A., & Hurmuzlu, Y. (2002). Generating pathologicalgait patterns via the use of robotic locomotion models.Journal of the European Society for Engineering andMedicine, 10/2, 135–46.

Florence, J.M., Pandya, S., King, W.M., Robison, J.D., Baty, J.,Miller, J.P., Schierbecker, J. & Signore, L.C. (1992). Intra-rater reliability of manual muscle test (Medical ResearchCouncil scale) grades in Duchenne’s muscular dystrophy.Physical Therapy, 72/2, 115–122.

Forner-Cordero, A., Koopman, H.J.F.M., &VanDer Helm, F.C.T. (2006). Describing gait as a sequence of states. Journal ofBiomechanics, 39/5, 948–957.

Fosang, A., & Baker, R. (2006). Amethod for comparing manualmuscle strength measurements with joint moments duringwalking. Gait and Posture, 24/4, 406–411.

Foster, J.P., Nixon, M.S., & Prügel-Bennett, A. (2003).Automatic gait recognition using area-based metrics. PatternRecognition Letters, 24/14, 2489–2497.

Fowler, E.G., Staudt, L.A., Greenberg, M.B., & Oppenheim, W.L. (2009). Selective control assessment of the lower extremity(SCALE): development, validation, and interrater reliabilityof a clinical tool for patients with cerebral palsy. Develop-mental Medicine and Child Neurology, 51/8, 607–614.

Gage, J., Schwartz, M., Koop, S., & Novacheck, T. (2009). Theidentification and treatment of gait problems in cerebralpalsy. In Clinics in Developmental Medicine. London: M.K.Press, Ed.

Ghazwan, A., Forrest, S.M., Holt, C.A., & Whatling, G.M.(2017). Can activities of daily living contribute to EMGnormalization for gait analysis? PLoS ONE, 12/4, e0174670.

Goldberg, E.J., & Neptune, R.R. (2007). Compensatory strate-gies during normal walking in response to muscle weaknessand increased hip joint stiffness. Gait and Posture, 25/3,360–367.

Gough, M., & Shortland, A.P. (2008). Can clinical gait analysisguide the management of ambulant children with bilateralspastic cerebral palsy? Journal of Pediatric Orthopaedics, 28/8, 879–883.

Gross, R., Leboeuf, F., Hardouin, J.B., Lempereur, M., Perrouin-Verbe, B., Remy-Neris, O., & Brochard, S. (2013). Theinfluence of gait speed on co-activation in unilateral spasticcerebral palsy children.Clinical Biomechanics, 28/3, 312–317.

Haefeli, J., Huie, J.R., Morioka, K., & Ferguson, A.R. (2017).Assessments of sensory plasticity after spinal cord injuryacross species. Neuroscience Letters, 652, 74–81.

Harato, K., Nagura, T., Matsumoto, H., Otani, T., Toyama, Y.,& Suda, Y. (2008). A gait analysis of simulated knee flexioncontracture to elucidate knee-spine syndrome. Gait andPosture, 28/4, 687–692.

S. Armand et al.: Mov Sport Sci/Sci Mot 2017, 98, 77–88 87

Hausdorff, J.M., & Alexander, N.B. (2005). Gait disorders:evaluation and management. New York: Taylor & Francis.

Helwig, N.E., Hong, S., Hsiao-Wecksler, E.T., & Polk, J.D.(2011). Methods to temporally align gait cycle data. Journalof Biomechanics, 44/3, 561–566.

Henriksen, M., Alkjaer, T., Lund, H., Simonsen, E.B., Graven-Nielsen, T., Danneskiold-Samsøe, B., & Bliddal, H. (2007).Experimental quadriceps muscle pain impairs knee jointcontrol during walking. Journal of Applied Physiology(Bethesda, Md.: 1985), 103/1, 132–139.

Henriksen, M., Rosager, S., Aaboe, J., & Bliddal, H. (2011).Adaptations in the gait pattern with experimental hamstringpain. Journal of Electromyography and Kinesiology, 21/5,746–753.

Hermens, H.J., Freriks, B., Disselhorst-Klug, C., & Rau, G.(2000). Development of recommendations for SEMG sensorsand sensor placement procedures. Journal of Electromyogra-phy and Kinesiology, 10/5, 361–374.

Hof, A.L. (1996). Scaling gait data to body size. Gait andPosture, 4, 222–223. DOI: 10.1016/0966-6362(95)01057-2.

Itoh, N., Kagaya, H., Saitoh, E., Ohtsuka, K., Yamada, J.,Tanikawa, H., Tanabe, S., Itoh, N., Aoki, T. & Kanada, Y.(2012). Quantitative assessment of circumduction, hiphiking, and forefoot contact gait using Lissajous figures.Japanese Journal of Comprehensive Rehabilitation Science,3, 78–84.

Jansen, K., De Groote, F., Aerts, W., De Schutter, J., Duysens,J., & Jonkers, I. (2014). Altering length and velocity feedbackduring a neuro-musculoskeletal simulation of normal gaitcontributes to hemiparetic gait characteristics. Journal ofNeuroEngineering and Rehabilitation, 11/1, 78.

Kendall, K.D., Patel, C., Wiley, J.P., Pohl, M.B., Emery, C.A., &Ferber, R. (2013). Steps toward the validation of the Trende-lenburgTest. Clinical Journal of Sport Medicine, 23/1, 45–51.

Kikuchi, T., Oda, K., & Furusho, J. (2009). Development of leg-robot for simulation of spastic movement with Compact MRFluid Clutch. 2009 IEEE International Conference onRobotics and Automation, 1903–1908.

Kikuchi, T., Oda, K., Yamaguchi, S., & Furusho, J. (2010). Leg-robot with MR clutch to realize virtual spastic movements.Journal of Intelligent Material Systems and Structures, 21/15, 1523–1529.

Kirtley, C.,Whittle, M.W., & Jefferson, R.J. (1985). Influence ofwalking speed on gait parameters. Journal of BiomedicalEngineering, 7(4), 282–288.

Knarr, B.A., Reisman, D.S., Binder-Macleod, S.A., &Higginson,J.S. (2013). Understanding compensatory strategies formuscle weakness during gait by simulating activation deficitsseen post-stroke. Gait and Posture, 38/2, 270–275.

Kuyinu, E.L., Narayanan, G., Nair, L.S., & Laurencin, C.T.(2016). Animal models of osteoarthritis: classification,update, and measurement of outcomes. Journal of Orthopae-dic Surgery and Research, 11, 19.

Lamb, P.F., & Stöckl, M. (2014). On the use of continuousrelative phase: review of current approaches and outline for anew standard. Clinical Biomechanics, 29/5, 484–493.

Leardini, A., Chiari, A., Della Croce, U., & Cappozzo, A. (2005).Human movement analysis using stereophotogrammetryPart 3. Soft tissue artifact assessment and compensation.Gait and Posture, 21/2, 212–225.

Leardini, A., Sawacha, Z., Paolini, G., Ingrosso, S., Nativo, R., &Benedetti, M.G. (2007). A new anatomically based protocolfor gait analysis in children.Gait and Posture, 26/4, 560–571.

Lenhoff, M.W., Santner, T.J., Otis, J.C., Peterson, M.G.,Williams, B.J. & Backus, S.I. (1999). Bootstrap predictionand confidence bands: a superior statistical method foranalysis of gait data. Gait & Posture, 9, 10–17.

Leurs, F., Bengoetxea, A., Cebolla, A.M., De Saedeleer, C., Dan,B., & Cheron, G. (2012). Planar covariation of elevationangles in prosthetic gait. Gait and Posture, 35/4, 647–652.

Li, Z., Tsagarakis, N.G., & Caldwell, D.G. (2013). Walkingpattern generation and stabilization of a humanoid robot withcompliant joints. Autonomous Robots, 35, 1–14.

Logan, D., Kiemel, T., Dominici, N., Cappellini, G., Ivanenko, Y.,Lacquaniti, F., & Jeka, J.J. (2010). The many roles of visionduring walking. Experimental Brain Research, 206/3, 337–350.

Lund,M.E., de Zee,M., Andersen,M.S., &Rasmussen, J. (2012).On validation of multibodymusculoskeletal models. Proceed-ings of the Institution of Mechanical Engineers, Part H.Journal of Engineering in Medicine, 226/2, 82–94.

Matjačić, Z., & Olen�sek, A. (2007). Biomechanical characteriza-tion and clinical implications of artificially induced crouchwalking: differences between pure iliopsoas, pure hamstringsand combination of iliopsoas and hamstrings contractures.Journal of Biomechanics, 40/3, 491–501.

Matjačić, Z., Olen�sek, A., & Bajd, T. (2006). Biomechanicalcharacterization and clinical implications of artificiallyinduced toe-walking: differences between pure soleus, puregastrocnemius and combination of soleus and gastrocnemiuscontractures. Journal of Biomechanics, 39/2, 255–266.

McMulkin, M.L., Gulliford, J.J., Williamson, R.V, & Ferguson,R.L. (2000). Correlation of static to dynamic measures oflower extremity range of motion in cerebral palsy and controlpopulations. Journal of Pediatric Orthopedics, 20/3, 366–9.

Moissenet, F., & Armand, S. (2015). Qualitative and quantita-tive methods of assessing gait disorders. In Orthopedicmanagement of children with cerebral palsy: a comprehensiveapproach. Ch 17. New York: Nova Science Publishers.

Morris, R., Lord, S., Bunce, J., Burn, D., & Rochester, L. (2016).Gait and cognition: mapping the global and discrete relation-ships in ageing and neurodegenerative disease. Neuroscienceand Biobehavioral Reviews, 64, 326–45.

Okumura, H., Okamoto, S., Ishikawa, S., Isogai, K., Yanagihara-Yamada, N., Akiyama, Y., & Yamada, Y. (2015). Exoskeletonsimulator of impaired ankle: simulation of spasticity and clonus.Lecture Notes in Electrical Engineering, 277, 209–214.

Olen�sek, A., Matjačic, Z., & Bajd, T. (2005). Muscle contractureemulating system for studying artificially induced pathologi-cal gait in intact individuals. Journal of Applied Biomechan-ics, 21/4, 348–358.

Orendurff, M.S., Segal, A.D., Berge, J.S., Flick, K.C., Spanier,D., & Klute, G.K. (2006). The kinematics and kinetics ofturning: limb asymmetries associated with walking a circularpath. Gait and Posture, 23/1, 106–111.

Pandy, M.G., & Andriacchi, T.P. (2010). Muscle and jointfunction in human locomotion. Annual Review of BiomedicalEngineering, 12, 401–433.

Pataky, T.C. (2010). Generalized n-dimensional biomechanicalfield analysis using statistical parametric mapping. Journal ofBiomechanics, 43/10, 1976–1982.

Perry J., Fontaine Jd., & Mulroy S. (1995). Findings in post-poliomyelitis syndrome. Weakness of muscles of the calf as asource of late pain and fatigue of muscles of the thigh afterpoliomyelitis. Journal of Bone& Joint Surgery, 77/8, 1148–53.

Picerno, P. (2017). 25 years of lower limb joint kinematics byusing inertial and magnetic sensors: a review of methodologi-cal approaches. Gait & Posture, 51, 239–246.

Pinzone, O., Schwartz, M.H., Thomason, P., & Baker, R. (2014).The comparison of normative reference data from differentgait analysis services. Gait and Posture, 40/2, 286–290.

Pohl, P.S., Duncan, P.W., Perera, S., Liu, W., Min Lai, S.,Studenski, S., & Long, J. (2002). Influence of stroke-relatedimpairments on performance in 6-minutewalk test. Journal ofRehabilitation Research and Development, 39/4, 1–6.

88 S. Armand et al.: Mov Sport Sci/Sci Mot 2017, 98, 77–88

Powden, C.J., Hoch, J.M., & Hoch, M.C. (2015). Reliability andminimal detectable change of the weight-bearing lunge test: asystematic review. Manual Therapy, 20/4, 524–32

Reinbolt, J.A., Schutte, J.F., Fregly, B.J., Koh, B.I, Haftka, R.T., George, A.D., & Mitchell, K. H. (2005). Determination ofpatient-specific multi-joint kinematic models through two-level optimization. Journal of Biomechanics, 38/3, 621–626.

Ren, L., Jones, R.K., & Howard, D. (2007). Predictive modellingof human walking over a complete gait cycle. Journal ofBiomechanics, 40/7, 1567–1574.

Renjewski, D., & Seyfarth, A. (2012). Robots in humanbiomechanics - a study on ankle push-off in walking. Bio-inspiration & Biomimetics, 7/3, 36005.

Riley, P.O., &Kerrigan, D.C. (2001). The effect of voluntary toe-walking on body propulsion. Clinical Biomechanics, 16/8,681–687.

Romkes, J., & Brunner, R. (2007). An electromyographicanalysis of obligatory (hemiplegic cerebral palsy) andvoluntary (normal) unilateral toe-walking.Gait and Posture,26/4, 577–586.

Sagawa, Y., Watelain, E., De Coulon, G., Kaelin, A., Gorce, P.,& Armand, S. (2013). Are clinical measurements linked to thegait deviation index in cerebral palsy patients? Gait andPosture, 38/2, 276–280.

Sangeux, M., & Armand, S. (2015). Kinematic deviations inchildren with cerebral palsy. In Orthopedic management ofchildren with cerebral palsy: a comprehensive approach. Ch18. New York: Nova science publishers.

Sangeux, M., & Polak, J. (2015). A simple method to choose themost representative stride and detect outliers. Gait andPosture, 41/2, 726–730.

Sangeux, M., Rodda, J., & Graham, H.K. (2015). Sagittal gaitpatterns in cerebral palsy: the plantarflexor-knee extensioncouple index. Gait and Posture, 41/2, 586–591.

Schmid, S., Schweizer, K., Romkes, J., Lorenzetti, S., &Brunner,R. (2013). Secondary gait deviations in patients with andwithout neurological involvement: a systematic review. Gaitand Posture, 37/4, 480–493.

Schwartz, M.H., & Rozumalski, A. (2008). The gait deviationindex: a new comprehensive index of gait pathology.Gait andPosture, 28/3, 351–357.

Schwartz, M.H., Trost, J.P., & Wervey, R.A. (2004). Measure-ment and management of errors in quantitative gait data.Gait and Posture, 20/2, 196–203.

Schwartz,M.H., Rozumalski, A., &Trost, J.P. (2008). The effectof walking speed on the gait of typically developing children.Journal of Biomechanics, 41/8, 1639–1650.

Schweizer, K., Cattin, P.C., Brunner, R., Müller, B., Huber, C.,Romkes, J. (2012). Automatic selection of a representativetrial from multiple measurements using principle componentanalysis. Journal of Biomechanics, 45/13, 2306–2309.

Simonsen, E.B., Moesby, L.M., Hansen, L.D., Comins, J., &Alkjaer, T. (2010). Redistribution of joint moments duringwalking in patients with drop-foot. Clinical Biomechanics,25/9, 949–952.

Stagni, R., Fantozzi, S., & Cappello, A. (2009). Doublecalibration vs. global optimisation: performance and effec-tiveness for clinical application. Gait & Posture, 29/1, 119–122.

Sutherland, D.H. (2002). The evolution of clinical gait analysis:part II kinematics. Gait and Posture, 16/2, 159–179.

Sutherland, D.H., Olshen, R., Cooper, L., & Woo, S.L. (1980).The development of mature gait. The Journal of Bone andJoint Surgery. American Volume, 62/3, 336–53.

Taylor, W.R., Kornaropoulos, E.I., Duda, G.N., Kratzenstein,S., Ehrig, R.M., Arampatzis, A., & Heller, M.O. (2010).Repeatability and reproducibility of OSSCA, a functionalapproach for assessing the kinematics of the lower limb. Gaitand Posture, 32/2, 231–236.

Thompson, J.A., Chaudhari, A.M.W., Schmitt, L.C., Best, T.M., & Siston, R.A. (2013). Gluteus maximus and soleuscompensate for simulated quadriceps atrophy and activationfailure during walking. Journal of Biomechanics, 46/13,2165–2172.

Valente, G., Taddei, F., & Jonkers, I. (2013). Influence of weakhip abductor muscles on joint contact forces during normalwalking: probabilistic modeling analysis. Journal of Biome-chanics, 46/13, 2186–2193.

Van Campenhout, A., Bar-On, L., Aertbeliën, E., Huenaerts, C.,Molenaers, G., & Desloovere, K. (2014). Can we unmaskfeatures of spasticity during gait in children with cerebralpalsy by increasing their walking velocity? Gait and Posture,39/3, 953–957.

van der Krogt, M.M., Doorenbosch, C.A.M., & Harlaar, J.(2007). Muscle length and lengthening velocity in voluntarycrouch gait. Gait and Posture, 26/4, 532–538.

van der Krogt, M.M., Delp, S.L., & Schwartz, M.H. (2012). Howrobust is human gait to muscle weakness? Gait and Posture,36/1, 113–119.

van der Krogt, M.M., Bar-On, L., Kindt, T., Desloovere, K., &Harlaar, J. (2016). Neuro-musculoskeletal simulation ofinstrumented contracture and spasticity assessment inchildren with cerebral palsy. Journal of NeuroEngineeringand Rehabilitation, 13/1, 64.

van Schie, C.H.M. (2008). Neuropathy: mobility and quality oflife. Diabetes/metabolism Research and Reviews, 24 (Suppl1), S45–51.

Viehweger, E., Berard, C., Berruyer, A., & Simeoni,M.C. (2007).Testing range of motion in cerebral palsy. Ann Readapt MedPhys, 50/4, 258–265.

Westberry, D.E., Davids, J.R., Davis, R.B., & de MoraisFilho, M.C. (2008). Idiopathic toe walking: a kinematic andkinetic profile. Journal of Pediatric Orthopedics, 28/3,352–358.

Wren, T.A.L., Gorton, G.E., Õunpuu, S., &Tucker, C.A. (2011).Efficacy of clinical gait analysis: a systematic review.Gait andPosture, 34/2, 149–153.

Wu, G., Cavanag, P.R., & Cavanagh, P.R. (1995). ISBrecommandations for standardization in the reporting ofkinematic data. Journal of Biomechanics, 10, 1257–61.

Cite this article as: Armand S, Moissenet F, de Coulon G, & Bonnefoy-Mazure A (2017) Identifying and understanding gaitdeviations: critical review and perspectives. Mov Sport Sci/Sci Mot, 98, 77–88