Replay-Based Incremental Learning Framework for Gesture Recognition Overcoming the Time-Varying Characteristics of sEMG Signals

Gesture recognition techniques based on surface electromyography (sEMG) signals face instability problems caused by electrode displacement and the time-varying characteristics of the signals in cross-time applications. This study proposes an incremental learning framework based on densely connected...

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Vydané v:	Sensors (Basel, Switzerland) Ročník 24; číslo 22; s. 7198
Hlavní autori:	Zhang, Xingguo, Li, Tengfei, Sun, Maoxun, Zhang, Lei, Zhang, Cheng, Zhang, Yue
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Switzerland MDPI AG 10.11.2024 MDPI
Predmet:	Accuracy Adult Algorithms class-increment Datasets DBSCAN Electrodes Electromyography Electromyography - methods Experiments Female Gestures Humans incremental learning Machine Learning Male Neural networks Neural Networks, Computer Pattern Recognition, Automated - methods sEMG Signal processing Signal Processing, Computer-Assisted time-varying characteristics time-varying characteristics DBSCAN sEMG class-increment incremental learning
ISSN:	1424-8220, 1424-8220
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