Reducing the Energy Consumption of sEMG-Based Gesture Recognition at the Edge Using Transformers and Dynamic Inference

Hand gesture recognition applications based on surface electromiographic (sEMG) signals can benefit from on-device execution to achieve faster and more predictable response times and higher energy efficiency. However, deploying state-of-the-art deep learning (DL) models for this task on memory-const...

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Detalhes bibliográficos

Principais autores:	Chen Xie, Alessio Burrello, Francesco Daghero, Luca Benini, Andrea Calimera, Enrico Macii, Massimo Poncino, Daniele Jahier Pagliari
Formato:	Artigo
Idioma:	Inglês
Publicado em:	MDPI AG 2023-02-01
coleção:	Sensors
Assuntos:	transformers sEMG gesture recognition deep learning embedded systems
Acesso em linha:	https://www.mdpi.com/1424-8220/23/4/2065
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