Muscle activation detection with surface electromyography: A combined signal processing and machine learning approach

Surface electromyography (sEMG) is a noninvasive method for monitoring muscle activity, essential in rehabilitation, prosthetics, sports, and medicine. However, current sEMG systems struggle with signal noise, unclear muscle activation detection, and limited adaptability. This study proposes a method combining signal processing and machine learning to enhance muscle activation detection. Experiments on EMG data from muscle contractions ( n = 140 per test, with 28 participants, five biceps curl repetitions, five wrist curl repetitions each) demonstrated 92.23% overall accuracy (3.83% false positives, 3.94% false negatives) for biceps tests and 91.11% overall accuracy (4.36% false positives, 4.53% false negatives) for wrist tests, and an average recall of 96% significantly outperforming traditional methods. This approach highlights potential applications in real-world biomedical settings from rehabilitation and prosthetics to sports science.