sEMG-Driven Hand Dynamics Estimation With Incremental Online Learning on a Parallel Ultra-Low-Power Microcontroller

Surface electromyography (sEMG) is a State-of-the-Art (SoA) sensing modality for non-invasive human-machine interfaces for consumer, industrial, and rehabilitation use cases. The main limitation of the current sEMG-driven control policies is the sEMG's inherent variability, especially cross-ses...

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Vydáno v:	IEEE transactions on biomedical circuits and systems Ročník 18; číslo 4; s. 810 - 820
Hlavní autoři:	Zanghieri, Marcello, Rapa, Pierangelo Maria, Orlandi, Mattia, Donati, Elisa, Benini, Luca, Benatti, Simone
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States IEEE 01.08.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Accuracy Adult Biomechanical Phenomena Continual learning Datasets Deep Learning Distance learning Electromyography embedded Female Fingers Hand Hand (anatomy) Hand - physiology Human computer interaction human-machine interaction human-machine interfaces Humans Incremental learning Kinematics Latency low-power Machine Learning Male Man-machine interfaces microcontroller Microcontrollers on-device learning Online instruction online learning parallel computing Power management prosthetics PULP Real time regression Signal Processing, Computer-Assisted Simultaneous discrimination learning temporal convolutional networks Tiny machine learning tinyML Training training-on-device ultra-low power
ISSN:	1932-4545, 1940-9990, 1940-9990
On-line přístup:	Získat plný text
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