Abstract
This work presents the evaluation of gait spatio-temporal and statistical parameters for automatic classification of human gait. A bundle of clinically relevant walk features is obtained from a cohort of healthy controls as well as neuro-impaired subjects and is normalized using dimensionless normalization to account for physiological variation like height, weight, etc. For feature selection and optimization, significant differences between the derived features from both groups are computed. A machine learning strategy is employed to train and classify these data into healthy and pathological group. The classifier reported a best classification accuracy of around 96% with both dimensional and dimensionless feature sets, with absolute minimum accuracy of just over 90%. The present work demonstrates the effectiveness of spatio-temporal and derived statistical features for gait classification. Extraction of such features is a relatively low-cost and less burdensome process in comparison with traditional approaches involving raw kinetic or kinematic parameters such as ground reaction force and bio-signals.
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Das, R., Khera, P., Saxena, S., Kumar, N. (2022). Automated Gait Classification Using Spatio-Temporal and Statistical Gait Features. In: Sharma, T.K., Ahn, C.W., Verma, O.P., Panigrahi, B.K. (eds) Soft Computing: Theories and Applications. Advances in Intelligent Systems and Computing, vol 1380. Springer, Singapore. https://doi.org/10.1007/978-981-16-1740-9_40
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DOI: https://doi.org/10.1007/978-981-16-1740-9_40
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