Development of electromyography signal classification for musculoskeletal disorders

Mohamad Shaidi, Labiq (2021) Development of electromyography signal classification for musculoskeletal disorders. Project Report. Universiti Teknikal Malaysia Melaka, Melaka, Malaysia. (Submitted)

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Abstract

Due to the extreme evolution of biomedical and healthcare applications, there are various methods for monitoring muscle health status. The electromyography signal , which has been used to detect the electrical signal produced by the muscles, is being employed in this study to detect the muscle condition of a musculoskeletal disorders (MSDs). A random sample of persons is chosen to assess their muscle health and identify if they are at risk of acquiring musculoskeletal disorders or already have them. By doing some exercises utilizing the Functional Range of Motion (FROM) task, the test can assess muscular fatigue. The data will be extracted in MATLAB, and the subjects will be separated into two groups: normal and musculoskeletal disorders. The machine learning technique which is Support Vector Machine (SVM) technique is will be used to distinguish between normal and musculoskeletal disorders as the classification method.

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