Beyond Falls: A Hybrid CNN–LSTM–Attention Framework for Pre-, Transition-, and Post-Fall Detection With Wearable Inertial Sensors

Falls are a leading cause of injury, disability, and mortality among older adults, with significant physical, psychological, and economic consequences. This paper presents a deep learning–based real-time fall detection system capable of identifying falls during pre-impact, post-impact, and tr...

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Principais autores:	Faraz Ahmad Bhatti, Qaiser Riaz, Bjorn Kruger
פורמט:	Artigo
שפה:	Inglês
יצא לאור:	IEEE 2025-01-01
סדרה:	IEEE Access
נושאים:	Deep learning for fall detection fall prevention multi-class falls multi-class human activity recognition (HAR) inertial measurement units (IMUs) inertial sensors
גישה מקוונת:	https://ieeexplore.ieee.org/document/11283000/
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