Predicting stride time variability from waist-mounted IMU data using machine learning: toward non-invasive gait stability monitoring in endurance running

Abstract Background Stride time variability (STV) is an important indicator of gait stability and motor control during running, yet its assessment typically depends on laboratory-based systems or distal sensor placement. This study evaluated whether a single waist-mounted inertial measurement unit (...

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Váldodahkkit:	Shane Fei Chen, Xianglin Zhao, Jason Tak-Man Cheung, Ming Zhang
Materiálatiipa:	Artigo
Giella:	Inglês
Almmustuhtton:	BMC 2026-05-01
Ráidu:	BMC Sports Science, Medicine and Rehabilitation
Fáttát:	Gait cycle IMU Machine learning XGBoost model Stride time variability
Liŋkkat:	https://doi.org/10.1186/s13102-026-01739-0
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