Quantitative gait analysis and prediction using artificial intelligence for patients with gait disorders

Quantitative gait analysis and prediction using artificial intelligence for patients with gait disorders

Quantitative Gait Analysis (QGA) is considered as an objective measure of gait performance. In this study, we aim at designing an artificial intelligence that can efficiently predict the progression of gait quality using kinematic data obtained from QGA. For this purpose, a gait database collected f...

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Bibliographic Details
Published in:Scientific reports Vol. 13; no. 1; p. 23099
Main Authors: Ben Chaabane, Nawel, Conze, Pierre-Henri, Lempereur, Mathieu, Quellec, Gwenolé, Rémy-Néris, Olivier, Brochard, Sylvain, Cochener, Béatrice, Lamard, Mathieu
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 28-12-2023
Nature Publishing Group
Nature Portfolio
Subjects:
631/114/2164
639/705/117
692/700/784
Artificial Intelligence
Biomechanical Phenomena
Computer Science
Fourier Analysis
Fourier transforms
Gait
Gait Analysis - methods
Humanities and Social Sciences
Humans
Kinematics
multidisciplinary
Neural networks
Neural Networks, Computer
Science
Science (multidisciplinary)
Signal and Image Processing
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Summary:Quantitative Gait Analysis (QGA) is considered as an objective measure of gait performance. In this study, we aim at designing an artificial intelligence that can efficiently predict the progression of gait quality using kinematic data obtained from QGA. For this purpose, a gait database collected from 734 patients with gait disorders is used. As the patient walks, kinematic data is collected during the gait session. This data is processed to generate the Gait Profile Score (GPS) for each gait cycle. Tracking potential GPS variations enables detecting changes in gait quality. In this regard, our work is driven by predicting such future variations. Two approaches were considered: signal-based and image-based. The signal-based one uses raw gait cycles, while the image-based one employs a two-dimensional Fast Fourier Transform (2D FFT) representation of gait cycles. Several architectures were developed, and the obtained Area Under the Curve (AUC) was above 0.72 for both approaches. To the best of our knowledge, our study is the first to apply neural networks for gait prediction tasks.
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PMCID: PMC10754876
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-023-49883-8