Consumer-priced wearable sensors combined with deep learning can be used to accurately predict ground reaction forces during various treadmill running conditions

• Published in: PeerJ (San Francisco, CA) Vol. 12; p. e17896
• Main Authors: Carter, Josh, Chen, Xi, Cazzola, Dario, Trewartha, Grant, Preatoni, Ezio
• Format: Journal Article
• Language: English
• Published: United States PeerJ. Ltd 29.08.2024; PeerJ Inc
• Subjects: Adult; Biomechanical Phenomena - physiology; Biomechanics; Computational Science; Consumer behavior; Data Mining and Machine Learning; Deep Learning; Distance running; Exercise Test - instrumentation; Exercise Test - methods; Female; Humans; IMU; Kinesiology; LSTM; Machine learning; Male; Medical research; Medicine, Experimental; Neural networks; Pressure insole; Running; Running - physiology; Sensors; Somatotropin releasing hormone; Sports Injury; Wearable Electronic Devices; Young Adult; Biomechanics; IMU; LSTM; Training load; Machine learning; Human locomotion; Distance running; Biofeedback; Pressure insole

Ground reaction force (GRF) data is often collected for the biomechanical analysis of running, due to the performance and injury risk insights that GRF analysis can provide. Traditional methods typically limit GRF collection to controlled lab environments, recent studies have looked to combine the e...