gesture recognition using surface electromyography and deep learning for prostheses hand: state-凯发k8一触即发

gesture recognition using surface electromyography and deep learning for prostheses hand: state-of-the-art, challenges, and future

发布时间:2022.04.13 14:08  访问次数:  作者:


amputation of the upper limb brings heavy burden to amputees, reduces their quality of life, and limits their performance in activities of daily life. the realization of natural control for prosthetic hands is crucial to improving the quality of life of amputees. surface electromyography (semg) signal is one of the most widely used biological signals for the prediction of upper limb motor intention, which is an essential element of the control systems of prosthetic hands. the conversion of semg signals into effective control signals often requires a lot of computational power and complex process. existing commercial prosthetic hands can only provide natural control for very few active degrees of freedom. deep learning (dl) has performed surprisingly well in the development of intelligent systems in recent years. the significant improvement of hardware equipment and the continuous emergence of large data sets of semg have also boosted the dl research in semg signal processing. dl can effectively improve the accuracy of semg pattern recognition and reduce the influence of interference factors. this paper analyzes the applicability and efficiency of dl in semg-based gesture recognition and reviews the key techniques of dl-based semg pattern recognition for the prosthetic hand, including signal acquisition, signal preprocessing, feature extraction, classification of patterns, post-processing, and performance evaluation. finally, the current challenges and future prospects in clinical application of these techniques are outlined and discussed.


tel:  021-63210200





上海地址: 上海市浦东新区广丹路222弄2号楼6层

厦门地址: 厦门市集美区百通科技园1号楼301-1室

上海傲意信息科技有限公司 凯发k8官网下载客户端中心的版权所有 © 2015-2023



  • 凯发k8一触即发-凯发k8官网下载客户端中心
  • 我要留言