Cargando…
Reduce Surface Electromyography Channels for Gesture Recognition by Multitask Sparse Representation and Minimum Redundancy Maximum Relevance
Surface electromyography- (sEMG-) based gesture recognition is widely used in rehabilitation training, artificial prosthesis, and human-computer interaction. The purpose of this study is to simplify the sEMG devices by reducing channels while achieving comparably high gesture recognition accuracy. W...
Autores principales: | Qu, Yali, Shang, Haoyan, Li, Jing, Teng, Shenghua |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8177973/ https://www.ncbi.nlm.nih.gov/pubmed/34136113 http://dx.doi.org/10.1155/2021/9929684 |
Ejemplares similares
-
Maximum Relevance Minimum Redundancy Dropout with Informative Kernel Determinantal Point Process
por: Saffari, Mohsen, et al.
Publicado: (2021) -
Data Augmentation of Surface Electromyography for Hand Gesture Recognition
por: Tsinganos, Panagiotis, et al.
Publicado: (2020) -
Minimum redundancy maximum relevance feature selection approach for temporal gene expression data
por: Radovic, Milos, et al.
Publicado: (2017) -
High-Performance Surface Electromyography Armband Design for Gesture Recognition
por: Zhang, Ruihao, et al.
Publicado: (2023) -
A View Transformation Model Based on Sparse and Redundant Representation for Human Gait Recognition
por: Ghebleh, Abbas, et al.
Publicado: (2020)