@inproceedings{720e86b48f2a455b8914527835bcddc1,
title = "Movement identification based on transient sEMGfor control of prosthesis",
abstract = "Researches on surface electromyography (sEMG) for upper-limb prosthesis control have been going on for several years. Most published studies on prosthesis usually use the steady-state sEMG or the transient sEMG for identification. However, the transient sEMG is less stable than steady-state sEMG. The nonstationarity in transient sEMG greatly affects the performance of myoelectric control. In this paper, we propose a method based on sparse representation to capture the characteristics of transient sEMG to identify movements. Experiment results show the proposed method extracts the variations in transient sEMG activity from different movements effectively. The proposed feature achieves a satisfactory classification rate, which outperforms the other features.",
keywords = "Feature extraction, Sparse representation, Temporal MMV Sparse Bayesian Learning (T-MSBL), Transient surface electromyography (sEMG)",
author = "Shuai Ding and Liang Wang and Sun, \{Zhan Peng\} and Gao, \{Wei Jin\} and Fang, \{Shou Long\}",
year = "2014",
doi = "10.4028/www.scientific.net/AMR.971-973.1651",
language = "英语",
isbn = "9783038351412",
series = "Advanced Materials Research",
publisher = "Trans Tech Publications Ltd",
pages = "1651--1654",
booktitle = "New Technologies for Engineering Research and Design in Industry",
address = "瑞士",
note = "2014 International Conference on Mechatronics and Intelligent Materials, MIM 2014 ; Conference date: 18-05-2014 Through 19-05-2014",
}