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

Area of Science:

• Neuroscience
• Biomechanics
• Motor Control

Background:

• Understanding the central nervous system's control of complex movements like walking is crucial.
• The role of motor units (MUs) in dynamic movements is unclear due to non-stationary conditions.
• This knowledge gap hinders studies on neural control of walking and development of neuro-rehabilitation strategies.

Purpose of the Study:

• To develop and validate a framework for decoding high-density electromyography (HD-EMG) into individual MU spike trains during walking.
• To examine neuromechanical delays (NMD) across different walking speeds.
• To establish MU-driven neuromusculoskeletal models to assess biomechanical consequences of decoded MU activity.

Main Methods:

• Developed a validated framework for HD-EMG decoding into individual MU spike trains during walking.
• Assessed static and adaptive decoding algorithms, validating against fine-wire intramuscular EMG (iEMG).
• Recorded HD-EMG, iEMG, motion capture, and ground reaction forces in five healthy adults walking at multiple speeds.

Main Results:

• Both static and adaptive decoding methods produced comparable MU spike trains.
• MU-driven models reproduced ankle joint moments with significantly lower error than conventional EMG-envelope models (p<0.05).
• Neuromechanical delays (NMD) decreased with increasing walking speed.

Conclusions:

• The study validates MU decomposition during walking, providing a framework for investigating spinal motor control in dynamic conditions.
• The MU-driven models accurately capture functional motor control, bridging cellular neural activity with biomechanical outcomes.
• This approach offers new avenues for neuro-rehabilitation, assistive technology, and understanding human locomotion.