EMG Signal Features Extraction of Different Arm Movement for Rehabilitation Device

• Main Authors: Jali, Mohd Hafiz, Ibrahim, 'Iffah Masturah, Sulaima, Mohamad Fani, Ahmad Izzuddin, Tarmizi, Wan Daud, Wan Mohd Bukhari
• Format: Article
• Language: English
• Published: Research India Publications 2014
• Subjects: TK Electrical engineering. Electronics Nuclear engineering
• Online Access: http://eprints.utem.edu.my/id/eprint/13571/1/Published_EMG_signal_Features_extraction_254_26864_-_IJAER__pp_11151-11162-1.pdf; http://eprints.utem.edu.my/id/eprint/13571/; http://www.ripublication.com
• Institution: Universiti Teknikal Malaysia Melaka

Rehabilitation device is used as an exoskeleton for people who had failure of their limb. Arm rehabilitation device may help the rehab program to who suffer from arm disability. The device used to facilitate the tasks of the program should improve the electrical activity in the motor unit and minimize the mental effort of the user. Electromyography (EMG) is the techniques to analyze the presence of electrical activity in musculoskeletal systems. The electrical activity in muscles of disable person is failed to contract the muscle for movements. To prevent the muscles from paralysis becomes spasticity the force of movements should minimize the mental efforts. To minimize the used of mental forced for disable patients, the rehabilitation device should analyze the surface EMG signal of normal people that can be implemented to the device. The signal is collected according to procedure of surface electromyography for non-invasive assessment of muscles SENIAM). The EMG signal is implemented to set the movements’ pattern of the arm rehabilitation device. The filtered EMG signal were extracted for features of Standard Deviation(STD), Mean Absolute Value(MAV), Root Mean Square(RMS) in time-domain. The extraction of EMG data is important to have the reduced vector in the signal features with less of error. In order to determine the best features for any movements, several trials of extraction methods are used by determining the features that can be used in classifier. The accurate features can be applied in future works of rehabilitation control system in real-time and classification of the EMG signal.