Real-Time Hand Motion Recognition Using sEMG Patterns Classification

被引：0
|
作者：
Crepin, Roxane [1 ]
Fall, Cheikh Latyr [1 ]
Mascret, Quentin [1 ]
Gosselin, Clement [2 ]
Campeau-Lecours, Alexandre [2 ]
Gosselin, Benoit [1 ]
机构：
[1] Univ Laval, Dept Comp & Elect Engn, Quebec City, PQ G1V 0A6, Canada
[2] Univ Laval, Dept Mech Engn, Quebec City, PQ G1V 0A6, Canada
来源：
2018 40TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC) | 2018年
基金：
加拿大自然科学与工程研究理事会;
关键词：
Myoelectric prostheses; sEMG signals; Analog Front-End; Classifier; Linear Discriminant Analysis; Real-Time;
D O I：
暂无
中图分类号：
R318 [生物医学工程];
学科分类号：
0831 ;
摘要：
Increasing performance while decreasing the cost of sEMG prostheses is an important milestone in rehabilitation engineering. The different types of prosthetic hands that are currently available to patients worldwide can benefit from more effective and intuitive control. This paper presents a real-time approach to classify finger motions based on surface electromyography (sEMG) signals. A multichannel signal acquisition platform implemented using components off the shelf is used to record forearm sEMG signals from 7 channels. sEMG pattern classification is performed in real time, using a Linear Discriminant Analysis approach. Thirteen hand motions can be successfully identified with an accuracy of up to 95.8% and of 92.7% on average for 8 participants, with an updated prediction every 192 ms.
引用
收藏
页码：2655 / 2658
页数：4
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