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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Detalles Bibliográficos
Autores principales: Xie, Chen, Burrello, Alessio, Daghero, Francesco, Benini, Luca, Calimera, Andrea, Macii, Enrico, Poncino, Massimo, Jahier Pagliari, Daniele
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9965939/
https://www.ncbi.nlm.nih.gov/pubmed/36850662
http://dx.doi.org/10.3390/s23042065

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9965939/
https://www.ncbi.nlm.nih.gov/pubmed/36850662
http://dx.doi.org/10.3390/s23042065

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