Human Movement Science 30 (2011) 966–975

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Human Movement Science

journal homepage: www.elsevier .com/locate/humov

Diagnosing fatigue in gait patterns by support vectormachines and self-organizing maps

Daniel Janssen a,⇑, Wolfgang I. Schöllhorn a, Karl M. Newell b, Jörg M. Jäger c,Franz Rost c, Katrin Vehof c

a Training and Movement Science, University of Mainz, Albert Schweitzer Strasse 22, 55099 Mainz, Germanyb Department of Kinesiology, The Pennsylvania State University, University Park, USAc Department of Sport Science, University of Muenster, Germany

a r t i c l e i n f o

Article history:Available online 30 December 2010

PsycINFO classification:37204160

Keywords:FatigueGaitPattern recognitionSupport vector machineNeural network

0167-9457/$ - see front matter � 2010 Elsevier B.Vdoi:10.1016/j.humov.2010.08.010

⇑ Corresponding author. Tel.: +49 6131 39 24560E-mail address: djanssen@uni-mainz.de (D. Jans

a b s t r a c t

The aim of the study was to train and test support vector machines(SVM) and self-organizing maps (SOM) to correctly classify gaitpatterns before, during and after complete leg exhaustion by isoki-netic leg exercises. Ground reaction forces were derived for 18 gaitcycles on 9 adult participants. Immediately before the trials 7–12,participants were required to completely exhaust their calves withthe aid of additional weights (44.4 ± 8.8 kg). Data were analyzedusing: (a) the time courses directly and (b) only the deviationsfrom each individual’s calculated average gait pattern. On aninter-individual level the person recognition of the gait patternswas 100% realizable. Fatigue recognition was also highly probableat 98.1%. Additionally, applied SOMs allowed an alternative visual-ization of the development of fatigue in the gait patterns over theprogressive fatiguing exercise regimen.

� 2010 Elsevier B.V. All rights reserved.

1. Introduction

Human movement, especially human gait, provides salient information about people, includingfeatures of individuality (e.g., Richardson & Johnston, 2005), gender (e.g., Kozlowski & Cutting,1977), age (e.g., Murray, Drought, & Kory, 1964), and pathologies (e.g., Holzreiter & Köhle, 1995).While these perceived characteristics are relatively stable and account considerably for individual

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.sen).

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properties of gait, there are other external and internal influences such as shoes (Oeffinger et al.,1999), emotions (e.g., Montepare, Goldstein, & Clausen, 1987), and music (Janssen et al., 2008), thatinfluence individual gait patterns under different conditions. A continuing challenge in the identifica-tion of individual and system features of human movement is that movement features appear to beboth particular (Schöllhorn, Nigg, Stefanyshyn, & Liu, 2002) and variable (Newell & Slifkin, 1998) tothe individual at the same time (Schöllhorn, 2000). The analysis of movement properties by patternrecognition techniques allows this exploration of both stable and variable properties of movement.For example, pattern recognition studies have revealed not only the high individuality of movements(Bauer & Schöllhorn, 1997; Schöllhorn & Bauer, 1998a, 1998b), but also the dependence of movementexecution properties on factors such as emotions (e.g., Janssen et al., 2008) or on the day of testing(Schöllhorn & Bauer, 1998b). The present experiment diagnosed through nonlinear pattern recogni-tion methods (support vector machines [SVM] and self-organizing maps [SOM]) if and how movementexecution in walking is altered by fatigue and contrasted the findings with those from a statisticalanalysis.

Several studies have examined the influence of fatigue on the kinematics and kinetics of movementexecution. For example, Gerlach et al. (2005) investigated how ground reaction forces (GRF) changedwith fatigue as induced by an exhaustive treadmill run in 90 female runners. They found a decreasedimpact peak and loading rates in all runners. Verkerke, Ament, Wierenga, and Rakhorst (1998) re-ported that fatigue effects were already observable in the form of step parameter variances duringan exhausting exercise, as the running pattern became irregular in the last phase of the exercise.Moreover, an increase in rearfoot motion seems to be affected directly by fatigue and not by a fati-gue-induced increase in step length (van Gheluwe & Madsen, 1997). Christina, White, and Gilchrist(2001) investigated the effects of fatiguing exercises of either the dorsiflexors or the invertors ofthe foot in running. They found that muscle fatigue can have a significant effect on the loading rates,peak magnitudes, and ankle joint motion during running, all features that may play a role in many typ-ical running injuries. The effect of fatigue on a broad set of other movement and muscle variables hasalso been reported (e.g., Gefen, Megido-Ravid, Itzchak, & Arcan, 2002; Helbostad, Leirfall, Moe-Nilssen,& Sletvold, 2007; Yoshino, Motoshige, Araki, & Matsuoka, 2004).

In most of the aforecited studies the recognition of fatigue in movement patterns was consideredusing linear classification methods with discrete movement parameters, and often without time-con-tinuous data. In this case time-continuous data are understood in terms of Schöllhorn et al. (2002)implying that the entire time courses are taken as input to the data analysis tools. It was shown bySchöllhorn et al. (2002) that this analysis is more powerful in predicting subject specific as well asgroup specific gait characteristics in comparison with time-discrete approaches and subject-selectedparameters. For the analysis of time-continuous data nonlinear classification tools from the field ofpattern recognition are appropriate instruments. These techniques have rarely been used in spite ofthe demonstration that gait patterns provide a rich potential for diagnosis (Begg & Kamruzzaman,2005; Nixon et al., 1999).

SVMs (Vapnik, 1995) are supervised machine learning methods used for classification and regres-sion (for a description see Wu, Wang, & Liu, 2007), and have increasingly been used in the classifica-tion of human characteristics, including face recognition (Gong, McKenna, & Psarrou, 2000), humangrip configurations under the scaling of object mass and size to the individual hand properties (Cesari,Chiaromonte, & Newell, 2007), and distinguishing the EEG patterns of individuals with different levelsof concussion (Cheng, Tutwiler, & Slobounov, 2008). They overcome several problems that can befound in classical neural networks when analyzing small datasets and seeking global minima (Bennett& Campbell, 2000). Previous studies have revealed that the classification of pathological gait analysisby SVM was superior to that of other machine learning methods using artificial neural networks andradial basis function neural networks (e.g., Lau, Tong, & Zhu, 2009). After compulsory amplitude nor-malization of the data, SVMs are normally trained with a larger segment of the data. The SVM hasknowledge on the class a pattern belongs to, and is trained to separate patterns of different classesfrom each other with a maximized margin. Therefore, the kernel trick (Aizerman, Braverman, &Rozonoer, 1964) is used, meaning that the data are mapped temporarily to a higher dimension whereit is easier to separate the classes. For this purpose several kernel functions exist. In this study only theRBF-kernel (nonlinear mapping with the aid of a radial basis function) is considered, due to its easier

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handling in most cases (e.g., less parameters and non-infinite parameter ranges in contrast to poly-nomial or sigmoid kernels) (Hsu et al., 2003). The RBF-kernel can handle nonlinear relationships be-tween the data and their class-membership, which is assumed for biological data (cf. Wu et al.,2007). After the training process the SVM can be tested with the remaining data in order to calculaterates of how well unknown patterns are linked with the correct classes. Hence, a rate of 100% meansthat all of the unknown test data are allocated to the correct classes by the SVM. A rate at chance level(e.g., 50% for two classes) shows that the SVM has failed to classify the patterns correctly. As SVMs aremerely able to separate and recognize two classes {+1, �1}, an extension called multiclass-SVM(Chang & Lin, 2001) is necessary if not only two states (e.g., with and without fatigue) should be ana-lyzed but more than two classes (e.g., the participants). A multiclass-SVM simply uses the trick to re-duce the given multiclass problem into multiple binary problems with two classes.

SOMs (Kohonen, 1982) on the other hand are unsupervised machine learning methods that are ableto automatically classify, separate, and distinguish high-dimensional input patterns by their similar-ities. From a statistical point of view SOMs can be seen as an hypothesis generating approach. A mostlytwo-dimensional graphical output space is used to illustrate the mapping of the data onto the neuronsof the net (for a description see Perl, 2004). Thereby the net is able to automatically find clusters ofsimilar input patterns within the dataset and map them onto groups of neighboring neurons. In thismanner similar gait patterns are clustered to similar regions and vice versa.

Especially for the classification with SOMs it might be helpful to reduce the dimension of the inputvectors (Haykin, 2008). Dimension reduction thereby tries to eliminate redundancy from the data byfeature extraction. The dimension D of a n � D matrix is mapped to a lower dimension d while tryingto retain the geometry of the data as much as possible. Several linear and nonlinear methods exist forthis purpose. Kernel Principal Component Analysis (kernel PCA) (Schölkopf, Smola, & Müller, 1998) is anonlinear variant of Principal Component Analysis and can be used in order to keep the data vectorssmall for further data-processing (for a description of kernel PCA see Wu et al., 2007).

Given the above perspectives the main questions of this study were the following: (i) Is there a sta-tistically significant difference between the gait patterns before and during fatigue on an inter-indi-vidual level? (ii) Are SVMs able to recognize persons from the gait patterns and is it possible torecognize the state of fatigue from a gait pattern on an inter-individual level? (iii) Does fatigue havean influence on the person recognition process over all stages of fatigue? (iv) Can SOMs retrace intra-individual changes in the gait patterns during fatigue and a possible regeneration process?

2. Methods

2.1. Participants

Nine healthy participants (25.9 ± 3.14 years of age) volunteered for the study, which consisted of asingle test with no intervention or training. Only males with considerable experience in sport (mainlytrack and field athletes) were chosen, as the mechanisms of fatigue vary with age and gender (Kent-Braun, Ng, Doyle, & Towse, 2002). This was done in order to acquire participants for a relativelyhomogenous group with the participants getting fatigued with a comparable amount of exercise.When arriving at the laboratory, participants were asked to dress in shorts and comfortable athleticclothes.

2.2. Procedure

After the explanation of the test setup, participants were required to become acquainted with theexperimental setup by simulating the test situation 3–5 times. The participants’ task was to walk bare-foot a distance of approximately 7 m from a fixed but self-chosen starting point in order to assure hit-ting a force plate (Kistler 9821B, 60 � 40 cm, 1000 Hz, Kistler, Swiss) with the third foot contact. For allparticipants the right foot was chosen in order to make the derived kinetic patterns (vertical groundreaction forces) more comparable (cf. Schöllhorn et al., 2002). If the force plate was not hit centrallythe trial was repeated. Two pairs of double light-barriers registered walking speed.

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To establish baseline values, participants performed six gait cycles. After baseline gait trials, 6 addi-tional trials were recorded immediately following a fatigue exercise protocol. Immediately before theparticipants started walking they were required to completely exhaust their m. soleus and m. gastroc-nemius by lifting and raising the rear foot standing on the toes. The participants were equipped with abarbell and additional weights on their shoulders (44.4 ± 8.8 kg). The weights were self-chosen (2 par-ticipants chose 60 kg, whereas 7 participants chose 40 kg additional weights). The number of repeti-tions until subjective exhaustion reached from 20 to 60 per subject and trial. The treatment stoppedwhen the participants were not able to lift the rear foot anymore. In addition, in the third round 6 fur-ther gait trials without any exercises were recorded after a 3-min break.

Hence, 18 gait cycles per participant were recorded from the three states before fatigue (B), duringfatigue (D), and after fatigue (A). Vertical ground reaction forces were filtered with a recursive 2nd or-der Butterworth low pass filter and a cutoff frequency of 100 Hz. The forces were then normalized intime to 100 sampling points covering 0–100% of the gait cycle by means of a mathematical linearinterpolation (cf. Jäger & Schöllhorn, 2007) and by amplitude to the interval [0, 1], in order to removeor minimize the possible influence of speed and body weight on the recognition process. This allowedretracing intra-individual changes and inter-individual comparisons at the same time.

2.3. Data analysis

Table 1 summarizes the organization of the data analysis. For comparison with the performance ofthe nonlinear analysis methods, a traditional linear statistical analysis by means of time discrete vari-ables was conducted first in order to investigate if the subjects’ gait patterns were affected and chan-ged by fatigue. Six commonly used time-discrete parameters from the vertical ground reaction forcesaccording to Giakas and Baltzopoulos (1997) were computed and analyzed statistically. These wereFLOAD1, FLOAD2, FLOAD3, TLOAD1, TLOAD2, and TLOAD3 describing the typical characteristics of the M-shaped curve, at least on a very coarse level. The parameters include the forces and elapsed gait cycletimes for the first (1) and the second (3) peak, and the ‘valley’ in-between (2).

The time-discrete data can be seen as a real subset and a subject-driven filter of time-continuousdata and it was anticipated that time-continuous data would provide additional information. Hence,we chose a nonlinear SVM with an RBF-kernel to test if the gait patterns contained information onthe person a pattern belongs to, and if it is possible to recognize the state of fatigue in the gait patternsfor the states before fatigue and during fatigue and for all states respectively (cf. Table 1). This was con-ducted using v-fold cross validation (Jain, Duin, & Mao, 2000). This means that the training set wasdivided into v subsets, trained with v-1 subsets and tested with the remaining one. This assures thefinding of optimal parameters (C, c) for the SVM, avoids overfitting, and delivers average and morereliable recognition rates if so desired (Hsu et al., 2003). In comparison to typical individual statisticalaveraging, all available gait patterns are considered in the classification process. These were 162 gaitpatterns.

Table 1Schematic overview of the study. The superscript shows which data were considered. (B) before fatigue, (D) during fatigue, (A)after fatigue.

Person recognition Recognition of fatigue

Inter-individual levelTime-discrete data – t-TestBD

Time-continuous dataSignal Approach SVMBD SVMBD

Deviations approach SVMBD SVMBD

Signal Approacha SVMBDA –

Intra-individual levelTime-continuous data – SOMBDA

(–) not analyzed.a In this case the deviations approach was omitted, as the signal approach delivered the best person recognition rates.

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On an inter-individual level both person recognition and recognition of fatigue on the basis of thekinetic data was conducted using only the kinetic data of the states before fatigue and during fatigue (cf.Table 1). For person recognition a multiclass-SVM with the ‘‘one-against-one’’ algorithm (Hsu & Lin,2002) was used. For the inter-individual recognition of fatigue in gait patterns, the data samples wereallocated to the two classes before fatigue (+1) and during fatigue (�1) and presented to a single SVM(Chang & Lin, 2001). Both person recognition and recognition of fatigue were accomplished using twocustom-developed data-preparation methods:

(a) The signal approach: The time course of all gait patterns was directly used as time continuousinput data.

(b) The deviations approach: For every single participant a synthetic model gait pattern was calcu-lated by statistically averaging all 18 participant’s gait patterns data point by data point (cf.Fig. 1a). For every participant individually, this synthetic model gait pattern was subtractedfrom all participant’s gait patterns in order to extract only intra-individual differences, or, inother words, to get only the deviations from the ‘average gait’ of the participant (cf. Fig. 1bfor one single example pattern). These deviations were then taken for further processing (cf.Fig. 1c). It has to be noted that in difference to Janssen et al. (2008) the deviations were acquirednot by subtracting from the overall average but from each individual’s average.

In addition a test was implemented to analyze the influence of fatigue on the individual character-istics of gait concerning all three conditions separately (cf. Table 1 middle). The idea behind this wasthat fatigue might alter the subjects’ gait patterns so much that individual characteristics may be af-fected. If this was the case, the person recognition rates would decrease for the during fatigue condi-tion, and if a regeneration took place, the rates would increase again in the after fatigue condition forthe group of nine participants.

On an intra-individual level it was of specific interest to investigate the similarity and changes inthe individual gait patterns before, during and after fatigue (cf. Table 1, bottom). Therefore kernel Prin-cipal Component Analysis (kernel PCA) was used for dimension reduction (van der Maaten, 2007) incombination with pre-trained SOMs for classification, offering a feasible visualization of the gait pat-terns’ similarities in a two-dimensional space (cf. Janssen et al., 2008). As the whole dataset was notsufficiently large, the SOM was pre-trained with similar gait data from previous studies (Janssen et al.,2008) and the data from this study.

3. Results

Walking speed did not change significantly under any condition during the experiment (p = .097;Friedman test) and so the data analysis focused on the structure of the movement patterns and thequestion if and how fatigue changed the kinetic properties of the gait patterns. The linear statisticalanalysis of the time-discrete parameters showed a statistically significant difference between thebefore fatigue and during fatigue conditions for five out of the six variables (FLOAD1: lower during fatigue,p < .001, df = 8; FLOAD2: higher during fatigue, p = .001, df = 8; FLOAD3: higher during fatigue, p = .049,df = 8; TLOAD1: later during fatigue, p = .003, df = 8; TLOAD3: later during fatigue, p = .004, df = 8; t-test).Only for the variable TLOAD2 there was no statistical difference (TLOAD2: p = .084, df = 8; t-test).

Nonlinear person recognition on the basis of the vertical ground reaction forces from the conditionsbefore fatigue and during fatigue was realizable with a rate of 100% with the SVM using the signal-approach. By means of the deviations approach this rate was 65.7%. The recognition of fatigue in gaitpatterns (on an inter-individual level as well) delivered a recognition rate of 96.3% for the signalapproach. The deviations approach led to a rate of 98.1% (cf. Table 2).

Regarding person recognition for all participants and all three conditions before, during and afterfatigue separately, the recognition rates of the SVM decreased from 100% (before fatigue) to 96.3%(during fatigue) and increased again to 98.1% (after fatigue).

A closer look on an intra-individual level revealed interesting fatigue processes for some individu-als. Considering the process of fatiguing, the SOM offered the possibility to visualize the changes in the

Fig. 1. Schematic depiction of the deviations approach. (a) For every participant a synthetic average model gait pattern wascalculated. (b) The model gait pattern was subtracted from all participant’s gait patterns (exemplified for one pattern). (c) Thedeviation from the ‘average gait’ of the participant was used for further processing.

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gait patterns over time from before fatigue to during fatigue to after fatigue for every participant indi-vidually. As the SOM was trained only once with a large number of gait patterns, the underlying struc-ture of the SOM was the same for all individuals. Structurally, the gait patterns of all participants weremapped on 10 � 10 neurons (one square demonstrates one neuron). Hereby a principle for SOMs ingeneral is neighborhood preservation: objects that are similar to each other in the original input data

Table 2Inter-individual recognition rates of the support vector machine on the basis of allindividuals.

Signal approach (%) Deviations approach (%)

Person recognition 100.0 65.7Recognition of fatigue 96.3 98.1

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space have to be similar in the SOM output space as well. In order to demonstrate the nonlinearity ofthe neuron output space the distance matrix of the neurons is overlaid with a changing grey scale.According to the number of neighbors each node is overlapped by means of several distance squaresto the neighbored neurons. Thereby a bright color denotes smaller distances, while a dark color rep-resents bigger distances. In a second step every individual’s spreading of the 18 intra-individual gaitpatterns onto the SOM was highlighted in order to retrace pattern changes over time for the individ-ual. By doing this for the whole group of participants two different characteristic behaviors could beobserved.

For each characteristic behavior an exemplary spread of one representative participant’s gait pat-terns over the SOM is given in Fig. 2. The figure shows the participant’s 18 gait patterns mapped onsingle neurons during the three fatigue stages (abbreviated with ‘‘B’’, ‘‘D’’, and ‘‘A’’). In the figure neigh-borhood can be interpreted as similarity of the gait patterns, and this similarity is even higher whenthe background color is brighter. In the first case the participants’ intra-individual gait patterns wererather but not exactly similar to each other in the before fatigue condition (see Fig. 2a, ‘‘B’’). The gaitpatterns during fatigue (Fig. 2a, ‘‘D’’) were then similar to each other, but very dissimilar to the patternsfrom the before fatigue condition (the patterns are located in another region). The patterns after fatigue(Fig. 2a ‘‘A’’) had the tendency to converge to the before fatigue cluster again. For the second charac-teristic case a similar trend was found, although gait patterns in the conditions during fatigue and afterfatigue were overlapping and in close neighborhood with a clear separation from the before fatiguecondition. In this case the participants’ gait pattern seemed to switch with the beginning of the fatigu-ing exercise and stayed in the ‘‘D–A’’-cluster until the end of the experiment. Fig. 2b illustrates this fora representative participant whose gait pattern switched suddenly with the during fatigue condition.

4. Discussion

The finding of a person recognition rate of 100% for the SVM using the signal approach emphasizesthe high individuality of fingerprint-like properties (signatures) in human movement. These findingsare consistent with former studies in which person recognition on the basis of kinetic and kinematicgait patterns was realizable with recognition rates of 93.1–100% for 13 females wearing different highheels (Schöllhorn et al., 2002), and 98.5% for 38 participants under different emotional conditions(Janssen et al., 2008). The dominance of individuality in movements has also been found for running

Fig. 2. Two examples of two exemplary participants’ changes of the gait patterns with a 10 � 10 SOM (B: before fatigue, D:during fatigue, A: after fatigue). Neighbored patterns are more similar than dispersed patterns. The superscript indicates theincidence of patterns onto the neuron. If patterns of more than one class are mapped onto a neuron, this is indicated by a dottedline. The neurons’ distance matrix is integrated into the figure and shows the real distances between the data vectors. It can bethought of as a 3D-landscape. Black planes indicate borders (hills), where distances are bigger, whereas white planes donateclusters (valleys) in which distances are smaller and data vectors are very similar. The map was pre-trained with data from thecurrent experiment and similar previous studies (Janssen et al., 2008), in order to increase the amount of training samples.

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(Schöllhorn & Bauer, 1998a), discus (Bauer & Schöllhorn, 1997) and javelin throwing (Schöllhorn &Bauer, 1998b).

The person recognition rate in this study using the deviations approach was expected to be muchlower, as this approach only regarded intra-individual changes by subtracting the most individualcharacteristics from the movement pattern. Here a rate of 65.7% could be found with the SVM. By con-trast, the deviations approach even increased person recognition in former studies (e.g., Janssen et al.,2008). However, here different basic preconditions were used, as the reference pattern was built bycalculating the mean of all available gait patterns from all participants, and was then subtracted fromall single gait patterns in order to extract the individual deviations from the reference gait pattern.These deviations contained salient information on the individuals’ movement characteristics, as per-son recognition was more successful using this approach. This contrasting outcome opens questions tothe common strategy of data filtering, as individual information may be too often removed by filters,averaging, or searching for group tendencies (e.g., Winter, 1990).

Surprising were the high recognition rates of fatigued gait patterns even on an inter-individual le-vel just on the basis of the ground reaction forces. With 96.3% for the signal approach and even 98.1%for the deviations approach the SVM was apparently able to distinguish gait patterns before and dur-ing fatigue by kinetic information over all participants. Here the advantage of the deviations approachis apparent. Intra-individual differences were extracted more obviously. Hence, advantages in compar-ison to group oriented diagnosis on the basis of averaged and filtered data can be seen, if individualdifferences are not removed by too strong filters, in order to guarantee better individual support,regardless of sportive or clinical intervention.

On an intra-individual level retracing sudden changes in the gait patterns with the beginning of fa-tigue was realizable with the SOM, as well as retracing the regeneration process after fatigue for someparticipants. The identification of two characteristic behaviors (cf. Fig. 2a and b) provides two majoraspects that we will have a closer view on. First, a clear separation of movement patterns recordedbefore fatigue (B) and during fatigue (D) was found in both characteristic behaviors. Secondly, oneindividual pattern group that separates movement patterns after fatigue (A) and one pattern groupthat does not separate during (D) and after fatigue trials (A) was observed. While the first aspectclearly provides evidence for a changed coordination with fatigue in all participants the second aspectcan be associated with specific regeneration on a coordinative level. Because in the fatigue-patterngroup with separated after-fatigue-patterns, some after-fatigue-patterns are mapped close to thebefore-fatigue trials, a recovery tendency on the coordinative level can be assumed. Due to the chosendesign and the accompanied duration of the after-fatigue-situation no complete recovery seemed tobe achieved in this fatigue-pattern group. However, whether the other pattern group that showedno separation of the after fatigue trials experienced a fundamental change in gait patterns or justwas not able to recover at all from the fatiguing exercise demands for further research with prolongedafter fatigue conditions. Because of the chosen elaborative approach subsequent investigations aboutthe significance and specificity of the differences between both fatigue-pattern groups by means of aconfirmatory approach would provide even further insight. Independent of the aspects of both patterngroups the chosen approach offers the possibility to analyze fatiguing processes on a more coordina-tive and holistic level as well as investigating dependencies between physiological parameters (e.g.,lactate, ammonium etc.) and coordination.

Even if the two dimensions of the SOM may not represent the dataset’s real intrinsic dimension, ourfindings suggest that the process of fatiguing is worthy of further study. For example, SOMs could beapplied as a control diagnosis tool for avoiding overtraining in sports disciplines, or as an instrumentfor the diagnosis of regeneration speed. Dependent on this, the density of exercises or length of breaksin training and therapy could be determined more precisely. Whether the fatiguing and regenerationprocess is based more on the muscles or on the central nervous system requires further research.

In summary, the results imply that the fundamental movement pattern of walking is not only indi-vidual on the one hand, but also highly situation-dependent on the other hand, as the characteristicsof gait patterns depend on fatigue states in this study, or on emotions (e.g., Janssen et al., 2008;Montepare et al., 1987) in former studies. This situation-dependence can be seen as a finer structureor characteristic within the ‘typical’ gait pattern of an individual within short time scales. This is sup-ported by the inspection of the individuality of gait in the three conditions before, during and after

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fatigue. As person recognition rates decreased with fatigue, individual gait patterns were altered sostrongly that they overlap with other individuals’ patterns, at least for a short time of regenerationfor some participants. These findings underline the importance of pattern recognition as a tool to ex-plore structures within movements. Although the linear statistical analysis showed that certainparameters changed within the gait patterns with fatigue, few implications could be drawn on thestructure of individuality and fatigue in gait in general. On the contrary, SVMs and SOMs provided richpotential of deeper, more precise and more continuous analysis of human movements under differentfine-structured questions with the option of further analysis.

The findings also open questions to common perspectives of motor learning and control. It appearsthat dominant individuality and situation-dependent movement execution combined with the factthat exact movement repetition is not possible (Bernstein, 1967; Hatze, 1986), leads to questionsabout traditional teaching and learning approaches that are oriented on general (person independent)and constant (time independent) models based on repetitive movements. For example, when in sportseach champion can be distinguished from another (Schöllhorn & Bauer, 1998) and each champion ischanging his or her movement pattern in each situation, which movement pattern should a traineror teacher orient on for his or her beginning athlete? Similar problems occur in physical therapy wheretreatment typically is oriented on ideal averages of assumingly normal people. Therefore, the findingsare relevant as well to a clinical context of movement (here prevention and rehabilitation) that typi-cally is: (i) oriented on person independent ideal (techniques); and (ii) acquired/learned by repetitionoriented approaches. As the situation-dependence of human movement organization suggests thatlearning, traditionally understood as a transition in a time independent minimum, is rather dependenton the time scale instead (Schöllhorn, Mayer-Kress, Newell, & Michelbrink, 2009), an effective learningapproach in sports and therapy will have to consider and cope with fluctuations that are caused byfatigue as well, according to the findings of this study. Group-oriented learning, training, interventionor therapy may, therefore, not support learners, athletes or patients optimally. Turning the table onecould even suggest the possibility to explicitly use fatigue as a further variation, at least in sportcontexts.

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