Feature extraction of muscle fatigue using surface electromyography (SEMG) technique

Khoo , Hui Ping (2014) Feature extraction of muscle fatigue using surface electromyography (SEMG) technique. Project Report. UTeM. (Submitted)

Text FEATURE EXTRACTION OF MUSCLE FATIGUE ON FOREARM USING SURFACE ELECTROMYOGRAPHY (sEMG) TECHNIQUE 24pages.pdf Download (359kB)

Abstract

Nowadays, musculoskeletal disorder has becoming a common disease in society. Muscle fatigue is one of the factor leads to musculoskeletal disorder. In this research, technique of surface electromyography (sEMG) signal detection and processing will be implemented. The main objective of this research is to extract features of muscle fatigue in order to evaluate the muscle fatigue condition of males and females. sEMG signal were collected from the forearm muscle - flexor carpi radialis of each volunteer. A group of 20 healthy university students were recruited in order to determine muscle fatigue occur in real life. A dynamic contraction and static contraction were implemented in order to understand the relationship between motion and fatigue and relationship between force and fatigue. Dynamic contraction experiment is done with subjects bent their wrist up to maximal joint angle; whereas static contraction experiment is done with different percentage of maximal voluntary contraction (MVC). For dynamic contraction, the feature of sEMG signal was extracted using time domain (RMS) and time-frequency domain (Scalogram). For static contraction, the feature of sEMG signal was extracted using time domain (RMS) and frequency domain (MDF). While analysing the time domain, it is found that the amplitude increased during fatigue in dynamic and static contraction experiment. For frequency domain, MDF are found to be decreased during fatigue in static contraction experiment. For time-frequency in terms of Scalogram, the energy distribution coefficients were found to be shifted to lower frequency as shown in the result and discussion part. Validity test is implemented in order to ensure the data collected is validated. Although the results were promising, there will be some limitations that need to be overcome in the future such as apply an online muscle fatigue progression test using Scalogram method for rehabilitation

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