Characterization, Indexing and Analysis of Motor Unit Activity During Muscular Effort by Multiple sEMG Integrated Signals

Background: Motor unit activity offers crucial information about muscle function and performance, providing invaluable data for diagnosis, treatment, and therapy. The parameters derived from this activity, such as the compound muscle action potential (CMAP) and motor unit number estimation (MUNE) extracted from the CMAP, are commonly obtained via electrical stimulation. While these methods provide specific insights into certain injuries and their impact on muscle function, their invasive nature presents a limitation. This work introduces an instrument capable of reconstructing and integrates multisignal sEMG (IEMG) as a high-fidelity indicator of CMAPs by convoluting MUAP (Motor Unit Action Potential) groups under controlled conditions, implementing an alternative noninvasive method with no electric stimulation to generate those sEMG-integrated multisignals (superficial IEMG) for our main objective, the characterization of muscle activity through the relationship between sEMG signals and force, an important indicator to complement CMAP. Results: The proposed system achieved a mean SNR of 14 ± 2.53 dB, a Pearson correlation of 0.907, and an nRMSE of 10.65%, results that robustly validate the precision of the IEMG model. This characterization accurately captures the electrophysiological dynamics of the quadriceps femoris during muscular effort, providing a continuous profile of muscle behavior. While this approach enables virtual MUNE estimation (averaging 85–147 motor units), Conclusions: Its primary significance lies in the methodology for characterize the muscle by integrated multisignals. This advancement provides a more accurate, streamlined tool for physiological assessment and physiotherapeutic diagnosis.