A Novel Unsupervised Adaptive Learning Method for Long-Term Electromyography (EMG) Pattern Recognition

Performance degradation will be caused by a variety of interfering factors for pattern recognition-based myoelectric control methods in the long term. This paper proposes an adaptive learning method with low computational cost to mitigate the effect in unsupervised adaptive learning scenarios. We pr...

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Detalles Bibliográficos
Autores principales: Huang, Qi, Yang, Dapeng, Jiang, Li, Zhang, Huajie, Liu, Hong, Kotani, Kiyoshi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2017
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5492218/
https://www.ncbi.nlm.nih.gov/pubmed/28608824
http://dx.doi.org/10.3390/s17061370

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5492218/
https://www.ncbi.nlm.nih.gov/pubmed/28608824
http://dx.doi.org/10.3390/s17061370

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