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EMG signal classification for human computer interaction: a review

With the ever increasing role of computerized machines in society, Human Computer Interaction (HCI) system has become an increasingly important part of our daily lives. HCI determines the effective utilization of the available information flow of the computing, communication, and display technologies. In recent years, there has been a 
tremendous interest in introducing intuitive interfaces that can recognize the user's body movements and translate them into machine commands. For the neural linkage with 
computers, various biomedical signals (biosignals) can be used, which can be acquired from a specialized tissue, organ, or cell system like the nervous system. Examples include Electro-Encephalogram (EEG), Electrooculogram (EOG), and Electromyogram (EMG). Such approaches are extremely valuable to physically disabled persons. Many attempts have been made to use EMG signal from gesture for developing HCI. EMG signal processing and controller work is currently proceeding in various direction including the 
development of continuous EMG signal classification for graphical controller, that enables the physically disabled to use word processing programs and other personal computer 
software, internet. It also enable manipulation of robotic devices, prosthesis limb, I/O for virtual reality games, physical exercise equipments etc. Most of the developmental area is based on pattern recognition using neural networks. The EMG controller can be programmed to perform gesture recognition based on signal analysis of groups of muscles 
action potential. This review paper is to discuss the various methodologies and algorithms used for EMG signal classification for the purpose of interpreting the EMG signal into computer command.