%0 Journal Article
%T A Review of Control Methods for Electric Power Wheelchairs Based on Electromyography Signals with Special Emphasis on Pattern Recognition
%A Phinyomark Angkoon
%A Phukpattaranont Pornchai
%A Limsakul Chusak
%J IETE Technical Review
%D 2011
%I 
%X Electric Power Wheelchairs (EPWs) are becoming increasingly important in assistive technology and rehabilitation devices. Normally EPWs are controlled by a joystick. However, this may not be suitable for disabled people who lack full control of their upper-limbs. Recent advances in the control of EPWs based on electromyography (EMG) signals are able to meet the needs of users with restricted limb movement and provide high performance control. Hence, EPWs controlled by EMG signals are highly appropriate for -elderly and disabled users. The purpose of this article is to review the state-of-the-art of EMG controlled EPWs and to present the achievements so far in this technology. A study of a variety of methods for EMG-based control in literature was studied here. Two types of control methods for EPWs, pattern recognition and hybrid recognition systems are discussed. Four major criteria are applied to compare the quality of control resulting from the use of these control methods: Accuracy of control, response time or real-time operation, robustness, and intuitiveness of control. Based on these four criteria, the use of the support vector machine classifier using features based on the time domain such as mean absolute value, waveform length, and zero crossing are suggested for the pattern recognition method. Furthermore, a combination of the pattern recognition and non-pattern recognition methods is recommended in order to increase the control commands by use of a small number of muscle positions.
%K Classification
%K Electric power wheelchair
%K Electromyography signal
%K Feature extraction
%K Hybrid system
%K Myoelectric control
%K Pattern recognition
%U http://tr.ietejournals.org/article.asp?issn=0256-4602;year=2011;volume=28;issue=4;spage=316;epage=326;aulast=Phinyomark