Advances in Electromyogram Signal Classification to Improve the Quality of Life for the Disabled and Aged People
- 1 , Afganistan
- 2 ,
Published On: 31 July 2010
Copyright: © 2020 Md. R. Ahsan, Muhammad I. Ibrahimy and Othman O. Khalifa. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Problem statement: The social demands for the Quality Of Life (QOL) are increasing with the exponentially expanding silver generation. To improve the QOL of the disabled and elderly people, robotic researchers and biomedical engineers have been trying to combine their techniques into the rehabilitation systems. Various biomedical signals (biosignals) acquired from a specialized tissue, organ, or cell system like the nervous system are the driving force for the entire system. Examples of biosignals 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 research is ongoing for the development of simple, robust, user friendly, efficient interfacing devices/systems for the disabled. The advancement can be observed in the area of robotic devices, prosthesis limb, exoskeleton, wearable computer, I/O for virtual reality games and physical exercise equipments. An EMG signal based graphical controller or interfacing system enables the physically disabled to use word processing programs, other personal computer software and internet. Results: Depending on the application, the acquired and processed signals need to be classified for interpreting into mechanical force 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 and applications. This review will be beneficial to the EMG signal researchers as a reference and comparison study of EMG classifier. For the development of robust, flexible and efficient applications, this study opened a pathway to the researchers in performing future comparative studies between different EMG classification methods.
- neural network
- fuzzy logic
- Hidden Markov Model (HMM)
- Independent Component Analysis (ICA)