Presently there has been much development of human‐like robotic hands, with most of this research dedicated to creation of prosthetics to aide a handicapped person with everyday tasks. Many of these prosthetics are beginning to use sensors that read muscle movement or nerve signals from the user to control the movement of the prosthetic limb. These prosthetic hands can greatly resemble the human hand in both aesthetic appearance and function, although these hands are only designed to aid the handicapped. As technology increases these designs can be modified to increase gripping capabilities of an average person; mimicking their actual hand movements while increasing strength. A design that can accomplish human like movements while increasing grip strength can be very useful for militaristic purposes, especially since grants have currently been presented to companies for the creation of an exoskeletal robotic suit.

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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-Encepha-logram (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.

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