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

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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Bibliographic Details
Main Authors: Chen Xie, Alessio Burrello, Francesco Daghero, Luca Benini, Andrea Calimera, Enrico Macii, Massimo Poncino, Daniele Jahier Pagliari
Format: Article
Language:English
Published: MDPI AG 2023-02-01
Series:Sensors
Subjects:
transformers
sEMG
gesture recognition
deep learning
embedded systems
Online Access:https://www.mdpi.com/1424-8220/23/4/2065

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https://www.mdpi.com/1424-8220/23/4/2065

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