SEMG features extraction for back muscle impairment

Muhamad Hafiy Syazwan , Zainoddin (2014) SEMG features extraction for back muscle impairment. Project Report. UTeM. (Submitted)

Text SEMG FEATURES EXTRACTION FOR BACK MUSCLE IMPAIRMENT 24pages.pdf Download (512kB)

Abstract

Nowadays, people give an extra effort to enhance their performance as compared to health care to improve the quality of life. They just focus and evaluate their health based on the physical fitness but neglected the condition of their muscle. If those things are left continues, it will cause a problems such as Low Back Pain (LBP) and this will affect their daily lives. Within the past decade, many researchers have come out with a new method and investigation to prevent from LBP because of growing demand from healthcare provider and realization of the important of back illness care. Besides that, due to the development of bio-medical and healthcare application, there are several ways that can be used to monitor the muscle status. One of them is by using Electromyography. This research is carried out in order to detect the muscle condition of back muscle and surface electromyography (SEMG) technique that have been used to detect the electrical signal produce by multifidus muscle. A group of 10 healthy people without LBP and 5 healthy people with LBP have been chosen in order to determine the muscle fatigue index during dynamic contraction activity which are sit to stand and stand to sit activities in real life. Data collected will be extracted by using time domain and frequency domain before classify the subjects into two group respectively, normal and LBP group. Neural network is used as classification method to classify between normal and LBP group. Based on classification part, neural network able to classify between normal between LBP groups but the classification result is not good enough due to the EMG machine specification and level of back pain faced by LBP subjects. As a conclusion, SEMG is able to extract features from EMG signal and the multifidus muscle can be used to produce an electrical signal to monitor muscle condition. Besides that, the finding and limitation of this study is good tools to enhance the EMG development in the future.

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