A Multi-Head Attention Transformer Model for Wearable in Situ Fall Detection

Falls represent a critical health hazard for elderly individuals, often leading to severe injuries and decreased quality of life. While existing fall detection systems predominantly rely on threshold-based algorithms or conventional machine learning approaches like CNNs and RNNs, they frequently exh...

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Autores principales:	Jingxiao Tian, Shengjie Xu, Peter Gerstoft, Mahasweta Sarkar, Alison A. Moore, Patrick Mercier, Christopher Paolini
Formato:	Artigo
Lenguaje:	Inglês
Publicado:	IEEE 2026-01-01
Colección:	IEEE Access
Materias:	Fall detection transformer model multi-head attention wearable computing edge computing FPGA implementation
Acceso en línea:	https://ieeexplore.ieee.org/document/11535463/
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

A Multi-Head Attention Transformer Model for Wearable in Situ Fall Detection

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