Recognition of hand grasp preshaping patterns applied to prosthetic hand electromyography control

被引：2

作者：

Yang, Dapeng ^{[1
]}

Zhao, Jingdong ^{[1
]}

Li, Nan ^{[1
]}

Jiang, Li ^{[1
]}

Liu, Hong ^{[1
]}

机构：

[1] State Key Laboratory of Robotics and System, Harbin Institute of Technology

来源：

Jixie Gongcheng Xuebao/Journal of Mechanical Engineering | 2012年 / 48卷 / 15期

关键词：

Electromyography control; Grasp preshaping; Pattern recognition; Prosthetic hand;

D O I：

10.3901/JME.2012.15.001

中图分类号：

学科分类号：

摘要：

It appears a big challenge when the multi-DOFs prosthetic hand is controlled by the electromyography (EMG) signals. A novel recognition method of the hand grasp preshaping patterns is proposed to the HIT-DLR prosthetic hand's EMG control. A new online detection method is designed to collect the accurate onset EMG signals of the grasp preshaping, which uses the Teager-Kaiser energy (TKE) operator and post processing to enlarge the changes of the EMG signal and deal with the spike noise, respectively. Focusing on 4 types of the hand preshaping patterns, different data segmentation methods, different features coming from the time-domain, frequency domain and time-frequency domain, and various classifiers are attempted to find the best classification accuracy. The waveform length and support vector machine are chosen, which can reach an accuracy of 95% and a response time less than 300 ms. The experiment of the prosthetic hand control shows that the hand can swiftly grasp the objects with various shapes. © 2012 Journal of Mechanical Engineering.

引用

页码：1 / 8

页数：7

共 16 条

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