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publications
Clinical Gait Analysis: Characterizing Normal Gait and Pathological Deviations Due to Neurological Diseases
Published in Sensors, 2023
This study addresses the characterization of normal gait and pathological deviations induced by neurological diseases, considering non-normalized knee angular kinematics in the sagittal plane.
Recommended citation: Hermez, L.; Halimi, A.; Houmani, N.; Garcia-Salicetti, S.; Galarraga, O.; Vigneron, V. "Clinical Gait Analysis: Characterizing Normal Gait and Pathological Deviations Due to Neurological Diseases", Sensors 2023, 23, 6566. https://doi.org/10.3390/s23146566
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An enhanced characterization of gait deviations in Hemiparesis by combining knee and ankle kinematics
Published in Gait & Posture, 2024
This study completes the method proposed in Sensors by considering sagittal ankle angular kinematics.
Recommended citation: L. Hermez, N. Houmani, S. Garcia-Salicetti, O. Galarraga, and V. Vincent. "An enhanced characterization of gait deviations in Hemiparesis by combining knee and ankle kinematics". Gait & Posture, 113:91–92, 2024. ISSN 0966-6362. https://doi.org/10.1016/j.gaitpost.2024.07.106
Gait Deviation and Neurological Diseases: a Comparative Study of Quantitative Measures
Published in Advances in Digital Health and Medical Bioengineering (EHB 2023), IFMBE Proceedings (111), Springer, 2024
This study compares the method proposed in Sensors with other state-of-the-art gait quality measures.
Recommended citation: Hermez, L., Houmani, N., Garcia-Salicetti, S., Galarraga, O., Vigneron, V. (2024). Gait Deviation and Neurological Diseases: A Comparative Study of Quantitative Measures. In: Advances in Digital Health and Medical Bioengineering. EHB 2023. IFMBE Proceedings, vol 111. Springer, Cham. https://doi.org/10.1007/978-3-031-62523-7_55
talks
Gait Deviation and Neurological Diseases: a Comparative Study of Quantitative Measures
Published:
This comparative study addresses the quantification of gait deviations from normal gait, and across different motor impairments induced by neurological diseases. We compared Gait Deviation Index and Gait Profile Score, well-known in the literature, to a novel 3D-metric based on Dynamic Time Warping (DTW) [1]. These deviation measures are analyzed following the same methodology, based on unsupervised learning, on the same database. Results show that our 3D-metric outperforms the others. This confirms that for finely quantifying deviations, it is crucial to consider different references to represent normal gait variability, as well as using an elastic distance (DTW) for matching two gait cycles.
Caractérisation de la marche normale et des déviations pathologiques dues aux maladies neurologiques : étude comparative des mesures de déviations de la marche
Published:
Objectifs
Cette étude vise à améliorer la caractérisation de la marche normale et des déviations pathologiques induites par des maladies neurologiques. Les objectifs sont la prise en compte de la variance présente au sein de la marche normale et de la dynamique temporelle des signaux de marche, ainsi que la quantification précise des déviations pathologiques par rapport à la marche d’individus sains. Enfin, la comparaison des performances de notre nouvelle méthode sera effectuée avec le Gait Deviation Index (GDI) et le Gait Profile Score (GPS).
An enhanced characterization of gait deviations in Hemiparesis by combining knee and ankle kinematics
Published:
Gait quality measures such as GDI [1] and GPS [2] rely on the Euclidian distance between time-normalized kinematic data to an average standard derived from healthy samples. Not accounting for temporal shifts can make these measures difficult to interpret or even unsuitable in some cases. For instance, in adult Hemiparesis, GDI and GPS are paradoxically often closer to the standard for the affected limb than for the unaffected limb [3]. We propose a novel method to characterize normal gait by identifying multiple representatives among the control group, and to compute deviations using an elastic metric taking into account temporal shifts.
Gait Deviation Assessment: from Signal to Image Analysis
Published:
In this paper, we propose a novel framework for gait quality assessment based on image analysis, extending the traditional signal-based approach. Specifically, we construct Cycle Dissimilarity Images (CDI) from raw gait signals. Such images summarize all local dissimilarities existing in the dynamics between a gait signal and one normal gait reference. Also, we construct a typical dissimilarity image, by matching each normal gait reference to itself. Then, we propose to quantify gait deviations by computing the distance between the CDIs and the typical dissimilarity images. Our results indicate that, compared to the signal-based approach, this new framework leads to a more precise gait deviation assessment, and a more refined characterization of motor impairments, as hemiparesis, tetraparesis, and paraparesis.
teaching
M1 DATAPAC - SIC7002
Master of Science course, Télécom SudParis, 2024
This is a short description of the lesson made in MSc DATAPAC