Open AccessA Review of Electromyography Signal Analysis Techniques for Musculoskeletal Disorders
Author(s) -
Tengku Nor Shuhada Tengku Zawawi,
Abdul Rahim Abdullah,
Mohd Hatta Jopri,
Tole Sutikno,
Norhashimah Mohd Saad,
Rubita Sudirman
Publication year - 2018
Publication title -
indonesian journal of electrical engineering and computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.241
H-Index - 17
eISSN - 2502-4760
pISSN - 2502-4752
DOI - 10.11591/ijeecs.v11.i3.pp1136-1146
Subject(s) - electromyography , readability , reliability (semiconductor) , bilinear interpolation , physical medicine and rehabilitation , cumulative trauma disorder , signal (programming language) , computer science , work (physics) , medicine , engineering , human factors and ergonomics , computer vision , poison control , mechanical engineering , power (physics) , physics , environmental health , quantum mechanics , programming language
Social Security Organisation (SOCSO) Malaysia has reported that the incidence of work related to musculoskeletal disorders (MSDs) has been growing planetary in the manufacturing industry. MSDs are the result of repetitive, forceful or awkward movements on our body and or body parts of bones, joints, ligaments and other soft tissues. Workplace pains and strains can be serious and disabling for workers, causing pain and suffering ranging from discomfort to severe disability. To overcome this problem, Electromyography is proper to use in Health Screening Program (HSP) it to monitor darn diagnose the muscle’s performance for their patient and know the exact localization of muscle pain. The previous researchers has been explore of several in EMG analysis techniques and features proposed in time, frequency and time-frequency domain analysis. This review of common EMG signal processing techniques is proposed by assembling from simple to complex analysis techniques to give the overview information for the other researcher. This is because; the suitable selection of a method and its features settings will ensure readability of the time-frequency representations and reliability of results. The strongest correspond with time-frequency characteristic and resolution also reducing cross term for bilinear will consider it as the optimal method.