Abstract
This study introduces an advanced computational model for simulating surface electromyography (sEMG) signals during muscle contractions. The model integrates five elements that simulate the chain of processes from motor intention to voltage variations over the skin. These elements include the motor control system, motor neurons, muscle fibers, biological tissues, and electrodes. sEMG signals were simulated for isotonic and isometric contractions under two force conditions and compared with real data obtained from elbow flexion experiments. The results demonstrate a high level of similarity between simulated and real signals, encompassing both temporal and spectral features. Additionally, the study reveals a correlation between muscle fiber type distribution and changes in the spectral distribution of the simulated signals. Potential applications of this research include the development of comprehensive sEMG databases and elucidating the relationship between sEMG signal characteristics and internal neuromuscular parameters. Future research aims to further explore these applications and enhance the model's performance by leveraging emerging technologies such as machine learning. This approach establishes a framework for simulating sEMG signals under tailored neuromuscular conditions and holds promise for advancing our understanding of muscular physiology and human motor control mechanisms.