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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Hlavní autoři:	Shane Fei Chen, Xianglin Zhao, Jason Tak-Man Cheung, Ming Zhang
Médium:	Artigo
Jazyk:	Inglês
Vydáno:	BMC 2026-05-01
Edice:	BMC Sports Science, Medicine and Rehabilitation
Témata:	Gait cycle IMU Machine learning XGBoost model Stride time variability
On-line přístup:	https://doi.org/10.1186/s13102-026-01739-0
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