SIGNAL ANALYSIS BASED ON HAND ACTIVITY: IMPLICATIONS FOR PROSTHETIC DEVELOPMENT
DOI:
https://doi.org/10.32896/ajmedtech.v4n2.17-32Keywords:
Electromyography, Signal Pattern Recognition, Hand Movement, Muscle Activation, Prosthetic ArmsAbstract
ABSTRACT: This study examines electromyography (EMG) signal pattern recognition to elucidate the relationship between EMG signals, hand movement, and biceps muscle force. The exercises will be performed at angles of 45°, 90°, and 120° relative to the elbow joint. Utilizing Matlab for EMG signal optimal features parameters, the research focuses on hand movements under varying loads (2kg, 4kg, and 6kg). Data acquisition involved tasks of lifting and holding, with EMG signals analysed across different phases of muscle activation. Results indicate a positive correlation between EMG signal amplitudes and both load and motion angles, revealing distinct muscle activation phases during lifting, holding, and releasing. The results show that the normalized average peak force at different loading levels is increases if the load increases, and if the load decreases the amplitude also decreases for trained and untrained subjects. The findings underscore the potential for these insights to inform the development of flexible prosthetic arms and assistive technologies.
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