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EnglishRecent works have demonstrated that the Independent Components (ICs) of simultaneously-recorded surface Electromyography (sEMG) recordings are more reliable in monitoring repetitive movements and better correspond with ongoing brain-wave activity than raw sEMG recordings. In this paper we propose to detect single muscle activation, when the arms reach a target, by means of ICs time-scale decomposition. Our analysis starts with acquisition of sEMG (surface EMG) signals; source separation is performed by a neural net-work that implements on Independent Component Analysis algorithm. In this way we obtain a signal set each representing single muscle activity. The wave-let transform, lastly, is utilised to detect muscle activation intervals.
| Titolo: | A New Approach to Detection of Muscle Activation by Independent Component Analysis and Wavelet Transform |
| Autori: | |
| Data di pubblicazione: | 2002 |
| Serie: | |
| Handle: | http://hdl.handle.net/20.500.12318/7482 |
| ISBN: | 978-3-540-44265-3 |
| Appare nelle tipologie: | 1.1 Articolo in rivista |
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