A Validated Framework for Decoding Motor Unit Firings and Resulting Ankle Moments During Walking

Understanding how the central nervous system controls complex movements, such as walking, remains a fundamental challenge. Although motor units (MUs) are well-studied in isometric tasks, their role in generating joint moments during functional dynamic movements is unclear, partly due to non-stationary conditions. Filling this knowledge gap is essential for studying neural control of walking at the cellular level and for guiding neuro-rehabilitation and robotic interventions. We developed a validated framework for decoding high-density electromyography (HD-EMG) into individual MU spike trains during walking. We assessed both static decoding and an adaptive algorithm that continuously tracks time-varying action potentials, validating the results against fine-wire intramuscular EMG (iEMG). We then examined neuromechanical delays (NMD) across walking speeds and established MU-driven neuromusculoskeletal models to determine the biomechanical consequences of the decoded MU firing patterns. Five healthy adults walked at multiple speeds while we recorded HD-EMG, iEMG, motion capture, and ground reaction forces. Both static and adaptive decompositions yielded comparable MU spike trains. Median rate of agreement was higher and false positives were lower for the adaptive approach, whereas false negatives were lower for the static approach. Although not statistically significant (p>0.05), these trends suggest the adaptive method may better handle nonstationarities. NMD decreased with speed, indicating coherent acceleration of neural-to-mechanical transmission. MU-driven models reproduced ankle joint moments with substantially lower normalized RMSE than conventional EMG-envelope models (p<0.05), with no difference between static and adaptive models. The close match between MU-derived and measured joint moments confirms that the decoded neural drive captures functional motor control rather than numerical artifacts. This study validates MU decomposition during walking and establishes a MU-driven model-based framework for investigating spinal motor control under dynamic, functionally relevant conditions. By bridging cellular-level neural activity with biomechanical outcomes, our approach opens new avenues for advancing neuro-rehabilitation, assistive technology, and our understanding of human locomotion.