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Vol. 167, Issue 3, March 2014, pp. 19-22




Classification of Hand Motions Using RBF Neural Network Based on Extracted Surface
Electromyography Signal

* Firas Al Omari, Guohai Liu, Yuhan Ding

Department of Automation and Control, School of Electrical and Information Engineering, Jiangsu University, Xuefu Rd 301#, Zhenjiang 212013, P. R. China
* Tel: +8615806109605

* E-mail: fomari6@gmail.com


Received: 31 December 2013 /Accepted: 28 February 2014 /Published: 31 March 2014

Digital Sensors and Sensor Sysstems


Abstract: Myoelectric prosthetic hand can be controlled by using surface electromyography (sEMG). In this paper, two sEMG signals were collected from forearm to classify five hand motions using radial basis function (RBF) neural network. Our experimental results demonstrate that the highest average classification accuracy was 93 % achieved using RBF based on root mean square (RMS), auto regressive model (AR), and energy of wavelet coefficients (WAVELET) combination.


Keywords: Signal processing, Artificial intelligence, Pattern recognition, Feature extraction, Wavelet analysis, Human-machine interface.


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