| Summary: | Exercise and repetitive movement fatigue are crucial to healthcare and rehabilitation. To improve the quality of rehabilitation and healthcare, precise evaluations and focused interventions are needed. Electromyography (EMG) is an essential technique used to measure muscle activity during various tasks. Despite its popularity, previous studies have yet to determine which EMG features are critical in classifying muscle contraction patterns across different physical activities and obtaining optimal performance. Therefore, this study aims to classify muscle contraction patterns during stepping exercises from sEMG and investigate the efficiency of Artificial Neural Networks as a classifier of this muscle contraction pattern. Sixty participants voluntarily participated in this stepping exercise and their EMG signal was recorded. The raw EMG signal undergoes filtering, rectification, and linear enveloping. Then, each signal from each participant was segmented into a relax signal and a contract signal. Time domain and frequency domain features were then retrieved from every relax and contract signal and fed into the ANN model. The results indicate that ANN yielded 96.5% accuracy performance in classifying the muscle contraction patterns from the sEMG signal. The findings from this study could contribute valuable insights to inform more effective and personalized rehabilitation services in identifying muscle contractions autonomously, thereby improving the overall mobility and quality of life for individuals facing muscle fatigue and financial problems. This study could help in strengthening the healthcare services mentioned in Ekonomi Madani's 17-Big Bolds, Malaysia. © 2024 IEEE.
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