Contributions to the understanding of gait control.

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Abstract

[Indexed for MEDLINE] 5. Comput Biol Med. 2025 Jan;184:109368. doi: 10.1016/j.compbiomed.2024.109368. Epub 2024 Nov 25. A novel gait quality measure for characterizing pathological gait based on Hidden Markov Models. Halimi A(1), Hermez L(1), Houmani N(2), Garcia-Salicetti S(1), Galarraga O(3). Author information: (1)SAMOVAR, Télécom SudParis, Institut Polytechnique de Paris, 91120 Palaiseau, France. (2)SAMOVAR, Télécom SudParis, Institut Polytechnique de Paris, 91120 Palaiseau, France. Electronic address: nesma.houmani@telecom-sudparis.eu. (3)Movement Analysis Laboratory, UGECAM Ile-de-France, 77170, Coubert, France. This study addresses the characterization of normal gait and pathological deviations caused by neurological diseases. We focus on the angular knee kinematics in the sagittal plane and we propose to exploit Hidden Markov Models to build a statistical model of normal gait. Such model provides a log-likelihood score that quantifies gait quality. Hence allowing to assess deviations of pathological cycles from normal gait. Our approach allows a refined characterization of motor impairments of three different patients' groups. In particular, it detects the affected lower limb in Hemiparetic patients. Comparatively to the Gait Variable Score and a Dynamic Time Warping-based metric, our results show that our statistical method is more effective for finely quantifying pathological deviations. Finally, we show the potential use of our methodology to assess therapeutic impacts during gait rehabilitation, which represents a promising avenue for improving patient care. Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved. DOI: 10.1016/j.compbiomed.2024.109368