Overcoming behavioral variability in electromyography signals by an adaptive incremental classification approach
Surface electromyography (sEMG)-based hand gesture recognition systems often experience significant performance degradation when deployed in heterogeneous populations, particularly among individuals with lifestyle-related physiological variations such as smoking or alcohol consumption. These variati...
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| Autori principali: | , , , , |
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| Natura: | Artigo |
| Lingua: | Inglês |
| Pubblicazione: |
American Association for the Advancement of Science (AAAS)
2025-01-01
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| Serie: | Computational and Structural Biotechnology Journal |
| Soggetti: | |
| Accesso online: | http://www.sciencedirect.com/science/article/pii/S2001037025003939 |
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