Automatic Detection of Gait Asymmetry

Maciej Cwierlikowski, Mercedes Torres


Gait analysis, and gait symmetry assessment in particular, are commonly adopted in clinical settings to determine sensorimotor fitness reflecting body’s ability to integrate multi-sensory stimuli, and use this information to induce ongoing motor commands. Inter-limb deviation can serve as a non-invasive marker of gait function to identify health conditions and monitor the effects of rehabilitation regimen. This paper examines the performance of machine learning methods (decision trees, k-NN, SVMs, ANNs) to learn and predict gait symmetry from kinetic and kinematic data of 42 participants walking across a range of speeds on treadmill. Classification was conducted for each speed independently with several feature extraction techniques applied. Subjects elicited gait asymmetry, yet ground reaction forces were more discriminative than joint angles. Walking speed affected gait symmetry with larger discrepancies registered at slower speeds; the highest F1 scores were noted at the slowest condition (decision trees: 87.35%, k-NN: 91.46%, SVMs: 88.88%, ANNs: 87.22%). None of the existing research has yet addressed ML-assisted assessment of gait symmetry across a range of walking speeds using both, kinetic and kinematic information. The proposed methodology was sufficiently sensitive to discern subtle deviations in healthy subjects, hence could facilitate an early diagnosis when anomalies in gait patterns emerge.


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