Advances in Electromyogram Signal Classification to Improve the Quality of Life for the Disabled and Aged People

Abstract

Problem statement: The social demands for the Quality Of Life (QOL) are increasing with the exponentially expanding silver generation. To i mprove the QOL of the disabled and elderly people, robotic researchers and biomedical engineers have b een trying to combine their techniques into the rehabilitation systems. Various biomedical signals (biosignals) acquired from a specialized tissue, or gan, or cell system like the nervous system are the driv ing force for the entire system. Examples of biosig nals include Electro-Encephalogram (EEG), Electrooculogram (EOG), Electroneurogram (ENG) and (EMG). Approach: Among the biosignals, the research on EMG signal processing and controlling is currently expanding in various directions. EMG signal based r esearch is ongoing for the development of simple, robust, user friendly, efficient interfacing device s/systems for the disabled. The advancement can be observed in the area of robotic devices, prosthesis limb, exoskeleton, wearable computer, I/O for virt ual reality games and physical exercise equipments. An EMG signal based graphical controller or interfacin g system enables the physically disabled to use word processing programs, other personal computer software and internet. Results: Depending on the application, the acquired and pro cessed signals need to be classified for interpreting into mechanical forc e or machine/computer command. Conclusion: This study focused on the advances and improvements on different methodologies used for EMG signal classification with their efficiency, flexibility a nd applications. This review will be beneficial to the EMG signal researchers as a reference and comparison st udy of EMG classifier. For the development of robust, flexible and efficient applications, this study ope ned a pathway to the researchers in performing futu re comparative studies between different EMG classific ation methods.