EMG Control for a Five-fingered Underactuated Prosthetic Hand Based on Wavelet Transform and Sample Entropy
EMG Control for a Five-fingered Underactuated Prosthetic Hand Based on Wavelet Transform and Sample Entropy
复制标题
DOI：
10.1109/iros.2006.282425
复制
发表时间：
2006-10
期刊：
影响因子：
--
通讯作者：
Jingdong Zhao;Zongwu Xie;Li Jiang;H. Cai;Hong Liu;G. Hirzinger
中科院分区：
文献类型：
--
作者：
Jingdong Zhao;Zongwu Xie;Li Jiang;H. Cai;Hong Liu;G. Hirzinger
提出了一种新型的五指欠驱动假手控制系统。假手控制部分基于肌电运动模式分类器，该分类器将基于变学习率的神经网络与小波变换和样本熵相结合。该运动模式分类器通过安装在拇指深屈肌、拇长屈肌和拇长伸肌上的三个电极测量表面肌电信号，成功地识别拇指、食指和中指的屈伸。此外，通过不断地控制单指的运动，假手可以实现更多的抓取动作，如力量抓取、指尖抓取等。实验结果表明，该分类器具有较高的识别能力，在仿生人机系统控制中具有很大的应用潜力
A new five-fingered underactuated prosthetic hand control system is presented in this paper. The prosthetic hand control part is based on an EMG motion pattern classifier which combines VLR (variable learning rate) based neural network with wavelet transform and sample entropy. This motion pattern classifier can successfully identify flexion and extension of the thumb, the index finger and the middle finger, by measuring the surface EMG signals through three electrodes mounted on the flexor digitorum profundus, flexor pollicis longus and extensor digitorum. Furthermore, via continuously controlling single finger's motion, the prosthetic hand can achieve more prehensile postures such as power grasp, fingertip grasp, etc. The experimental results show that the classifier has a great potential application to the control of bionic man-machine systems because of its high recognition capability