Pattern Recognition of Hand Movements with Low Density sEMG for Prosthesis Control Purposes

被引：0

作者：

Villarejo, J. J. ^{[1
]}

Frizera, A. ^{[1
]}

Bastos, T. F. ^{[1
]}

Sarmiento, J. F. ^{[2
]}

机构：

[1] Univ Fed Espirito Santo, Programa Posgrad Engn Eletr, Vitoria, Brazil

[2] Univ Fed Espirito Santo, Doutorado RENORBIO, Vitoria, Brazil

来源：

2013 IEEE 13TH INTERNATIONAL CONFERENCE ON REHABILITATION ROBOTICS (ICORR) | 2013年

关键词：

sEMG; multifunction myoelectric control system; low level movements; pattern recognition; isometric task;

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：

This paper presents a study related to the identification of different hand gestures from EMG signals from forearm muscles, to be used as human machine interface system in a hand prosthesis. The capture of EMG signals was performed with healthy people during different hand gestures related to the fingers flexion -individual and pairs-and flexion / extension and grasp grisp, organized into four categories. The low-level and low-density of sEMG signals was taking into account. Different characteristics were studied based on time and frequency, and were subsequently combined into pairs with fractal analysis, used for low level schemes. The results showed 95.4% higher than recognitions.

引用

页数：6

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