Cargando…
Feature-Level Fusion of Surface Electromyography for Activity Monitoring
Surface electromyography (sEMG) signals are commonly used in activity monitoring and rehabilitation applications as they reflect effectively the motor intentions of users. However, real-time sEMG signals are non-stationary and vary to a large extent within the time frame of signals. Although previou...
Autores principales: | Xi, Xugang, Tang, Minyan, Luo, Zhizeng |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2018
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5855029/ https://www.ncbi.nlm.nih.gov/pubmed/29462968 http://dx.doi.org/10.3390/s18020614 |
Ejemplares similares
-
Evaluation of Feature Extraction and Recognition for Activity Monitoring and Fall Detection Based on Wearable sEMG Sensors
por: Xi, Xugang, et al.
Publicado: (2017) -
Feature Extraction of Surface Electromyography Using Wavelet Weighted Permutation Entropy for Hand Movement Recognition
por: Liu, Xiaoyun, et al.
Publicado: (2020) -
MSFF-Net: Multi-Stream Feature Fusion Network for surface electromyography gesture recognition
por: Peng, Xiangdong, et al.
Publicado: (2022) -
Gesture Recognition Based on Multiscale Singular Value Entropy and Deep Belief Network
por: Li, Wenguo, et al.
Publicado: (2020) -
Flexible Electrode by Hydrographic Printing for Surface Electromyography Monitoring
por: Zeng, Xiong, et al.
Publicado: (2020)