Cargando…
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...
Autores principales: | Huang, Qi, Yang, Dapeng, Jiang, Li, Zhang, Huajie, Liu, Hong, Kotani, Kiyoshi |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2017
|
Materias: | |
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 |
Ejemplares similares
-
putEMG—A Surface Electromyography Hand Gesture Recognition Dataset
por: Kaczmarek, Piotr, et al.
Publicado: (2019) -
User-Independent EMG Gesture Recognition Method Based on Adaptive Learning
por: Zheng, Nan, et al.
Publicado: (2022) -
Improved Network and Training Scheme for Cross-Trial Surface Electromyography (sEMG)-Based Gesture Recognition
por: Dai, Qingfeng, et al.
Publicado: (2023) -
Surface Electromyography in Physiotherapist Educational Program in France: Enhancing Learning sEMG in Stretching Practice
por: Portero, Pierre, et al.
Publicado: (2020) -
LST-EMG-Net: Long short-term transformer feature fusion network for sEMG gesture recognition
por: Zhang, Wenli, et al.
Publicado: (2023)