Daily changes of individual gait patterns identified by means of support vector machines

Horst, Fabian; Kramer, Frederik; Schafer, Bradley; Eekhoff, Alexander; Hegen, Patrick; Nigg, Benno M.; Schöllhorn, Wolfgang I.

doi:10.1016/j.gaitpost.2016.07.073

Cited by 36 publications

(49 citation statements)

References 25 publications

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“…However, advantages for more sophisticated model architectures (like fully-connected model using a higher numbers of neurons or deep convolutional neural networks) can be observed in higher prediction accuracies (Table 1 and Supplementary Table ST1) and even more significant in the higher robustness of the model predictions against noise perturbations on the test data (Figure 3, Table 2 and Supplementary Table ST2). Because variability within individuals [26,28] as well as variability due to differences between individuals [55,27], genders [7] and ages [16] is an inherent feature of human motor control, prediction accuracy and model robustness are both essential for the development of reliable clinical applications using machine learning. Consequently, the present results suggest high potential of state-of-the-art non-linear methods such as DNNs compared to linear methods.…”

Section: Discussionmentioning

confidence: 99%

Explaining the unique nature of individual gait patterns with deep learning

Horst

Lapuschkin

Samek

et al. 2019

Sci Rep

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196

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Machine learning (ML) techniques such as (deep) artificial neural networks (DNN) are solving very successfully a plethora of tasks and provide new predictive models for complex physical, chemical, biological and social systems. However, in most cases this comes with the disadvantage of acting as a black box, rarely providing information about what made them arrive at a particular prediction. This black box aspect of ML techniques can be problematic especially in medical diagnoses, so far hampering a clinical acceptance. The present paper studies the uniqueness of individual gait patterns in clinical biomechanics using DNNs. By attributing portions of the model predictions back to the input variables (ground reaction forces and full-body joint angles), the Layer-Wise Relevance Propagation (LRP) technique reliably demonstrates which variables at what time windows of the gait cycle are most relevant for the characterisation of gait patterns from a certain individual. By measuring the time-resolved contribution of each input variable to the prediction of ML techniques such as DNNs, our method describes the first general framework that enables to understand and interpret non-linear ML methods in (biomechanical) gait analysis and thereby supplies a powerful tool for analysis, diagnosis and treatment of human gait.

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Section: Discussionmentioning

confidence: 99%

Explaining the unique nature of individual gait patterns with deep learning

Horst

Lapuschkin

Samek

et al. 2019

Sci Rep

Self Cite

196

183

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“…The distinction of intra-individual gait patterns from repeated measurement sessions indicates continuous changes of gait patterns that appear naturally without any intervention or injury. Accordingly, natural changes of gait patterns can be observed within a single day [40] and the intrinsic persistence of gait patterns is less than often assumed in gait analysis [43]. Supported by previously stated good reliability/repeatability [43], biomechanical diagnoses and therapeutic interventions typically assume that individual gait patterns are near constant without an intervention or injury [1].…”

Section: Discussionmentioning

confidence: 99%

“…Both are supervised learning models for the recognition of patterns and regularities in data. While SVM represent an well-established model for the classification of gait patterns based on joined vectors of time-continuous kinematic and kinetic data [38,40,50,51], KBDR is a recently developed classification approach that has never been applied for the analysis/classification of human movements. While KBDR showed higher classification accuracies than SVM and other models [47], especially on data sets with a small sample size but high dimensions, it seemed to be a promising model for the given classification problem.…”

Section: Methodsmentioning

confidence: 99%

“…In this context, the distinction of individual gait patterns [34] and the identification of situational characteristics like emotions [37] or fatigue [38] within intra-individual gait patterns provided further clarification for deterministic features in human movements and thereby indicated advantages of an analysis and treatment of human gait based on individual and situational needs [39,40]. Nevertheless, up to now, it is uncertain how complex deterministic and stochastic processes on multiple levels of movement organization and time-scales affect biomechanical analysis of the individual’s average gait stride patterns or rather the clinical decision-making based on it.…”

Section: Introductionmentioning

confidence: 99%

“…In particular, the level of intrinsic persistence of gait patterns has not been well detailed [43]. Previously, the identification of intra-individual changes of gait patterns between days indicated that the intrinsic persistence of gait patterns is smaller than often assumed in gait analysis [40]. Accordingly, we tested the hypothesis that gait patterns as well as their persistence over time are predominantly determined by deterministic processes that lead to time-dependent behavior in terms of natural temporal changes of gait patterns between repeated measurement sessions within a day and different degrees of changes in different time-scales.…”

Section: Introductionmentioning

confidence: 99%

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Intra-individual gait patterns across different time-scales as revealed by means of a supervised learning model using kernel-based discriminant regression

et al. 2017

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ObjectiveTraditionally, gait analysis has been centered on the idea of average behavior and normality. On one hand, clinical diagnoses and therapeutic interventions typically assume that average gait patterns remain constant over time. On the other hand, it is well known that all our movements are accompanied by a certain amount of variability, which does not allow us to make two identical steps. The purpose of this study was to examine changes in the intra-individual gait patterns across different time-scales (i.e., tens-of-mins, tens-of-hours).MethodsNine healthy subjects performed 15 gait trials at a self-selected speed on 6 sessions within one day (duration between two subsequent sessions from 10 to 90 mins). For each trial, time-continuous ground reaction forces and lower body joint angles were measured. A supervised learning model using a kernel-based discriminant regression was applied for classifying sessions within individual gait patterns.Results and discussionDiscernable characteristics of intra-individual gait patterns could be distinguished between repeated sessions by classification rates of 67.8 ± 8.8% and 86.3 ± 7.9% for the six-session-classification of ground reaction forces and lower body joint angles, respectively. Furthermore, the one-on-one-classification showed that increasing classification rates go along with increasing time durations between two sessions and indicate that changes of gait patterns appear at different time-scales.ConclusionDiscernable characteristics between repeated sessions indicate continuous intrinsic changes in intra-individual gait patterns and suggest a predominant role of deterministic processes in human motor control and learning. Natural changes of gait patterns without any externally induced injury or intervention may reflect continuous adaptations of the motor system over several time-scales. Accordingly, the modelling of walking by means of average gait patterns that are assumed to be near constant over time needs to be reconsidered in the context of these findings, especially towards more individualized and situational diagnoses and therapy.

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Effects of contextual interference and differential learning on performance and mental representations in a golf putting task

Mousavi,

Saberi Kakhki,

Fazeli

et al. 2024

European Journal of Sport Science

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It is widely accepted that mental representations can have an important influence on motor performance. Although differences in mental representations of motor tasks have been reported between novices and experts, little is known about their development as a function of motor learning approaches. The aim of this study was to compare the effects of contextual interference (CI) and differential learning (DL) on the performance and mental representations in a golf putting task. A total of 40 participants were randomly assigned into four groups: blocked contextual interference (BCI), random contextual interference (RCI), DL, and control. First, the participant's initial mental representation level was tested by means of the structural dimensional analysis of mental representation. Then, the participant's initial performance level was tested by 12 golf‐putting trials from 2.44 m. During the acquisition phase, participants practiced golf putting according to their grouping for three consecutive days with 10 blocks of 12 trials per day. No intervention was applied for the control group. The retention‐tests were performed 72 h after the last acquisition day. In addition, a transfer test to a novel distance outside the acquired range (4 m) was performed immediately after the retention‐test. The results of the putting performance in the retention test showed that RCI and DL performed better compared to BCI and the control group (all p < 0.05). In the transfer test, BCI and RCI outperformed the control group (all p < 0.05), but both were further outperformed by the DL group (all p < 0.05). Moreover, the DL group showed a more structured mental representation than the other groups during the retention test. These results indicated that DL used a different underlying mechanism that resulted in different levels of performance during transfer and a more structured mental representation compared with CI.

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Daily changes of individual gait patterns identified by means of support vector machines

Cited by 36 publications

References 25 publications

Explaining the unique nature of individual gait patterns with deep learning

Explaining the unique nature of individual gait patterns with deep learning

Intra-individual gait patterns across different time-scales as revealed by means of a supervised learning model using kernel-based discriminant regression

Effects of contextual interference and differential learning on performance and mental representations in a golf putting task

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