Towards reducing the impacts of unwanted movements on identification of motion intentions

详细信息    查看全文

• 作者：Xiangxin Li^a ; ^b ; ¹Author Vitae ; Shixiong Chen^a ; ^b ; ¹Author Vitae ; Haoshi Zhang^a ; ^bAuthor Vitae ; Oluwarotimi Williams Samuel^a ; ^bAuthor Vitae ; Hui Wang^a ; ^bAuthor Vitae ; Peng Fang^a ; ^bAuthor Vitae ; Xiufeng Zhang^cAuthor Vitae ; Guanglin Li^a ; ^b ; ^{gl.li@siat.ac.cn" class="auth_mail" title="E-mail the corresponding author}Author Vitae
• 关键词：Myoelectric prosthesis ; Robustness of myoelectric control ; Non-trained movement ; EMG pattern recognition
• 刊名：Journal of Electromyography and Kinesiology
• 出版年：2016
• 出版时间：June 2016
• 年：2016
• 卷：28
• 期：Complete
• 页码：90-98
• 全文大小：1580 K

文摘

Surface electromyogram (sEMG) has been extensively used as a control signal in prosthesis devices. However, it is still a great challenge to make multifunctional myoelectric prostheses clinically available due to a number of critical issues associated with existing EMG based control strategy. One such issue would be the effect of unwanted movements (UMs) that are inadvertently done by users on the performance of movement classification in EMG pattern recognition based algorithms. Since UMs are not considered in training a classifier, they would decay the performance of a trained classifier in identifying the target movements (TMs), which would cause some undesired actions in control of multifunctional prostheses. In this study, the impact of UMs was systemically investigated in both able-bodied subjects and transradial amputees. Our results showed that the UMs would be unevenly classified into all classes of the TMs. To reduce the impact of the UMs on the performance of a classifier, a new training strategy that would categorize all possible UMs into a new movement class was proposed and a metric called Reject Ratio that is a measure of how many UMs is rejected by a trained classifier was adopted. The results showed that the average Reject Ratio across all the participants was greater than 91%, meanwhile the average classification accuracy of TMs was above 99% when UMs occurred. This suggests that the proposed training strategy could greatly reduce the impact of UMs on the performance of the trained classifier in identifying the TMs and may enhance the robustness of myoelectric control in clinical applications.