Identification of Motor Unit Twitch Properties in the Intact Human In Vivo

Restoring natural motor function in neurologically injured individuals is challenging, largely due to the lack of personalization in current neurorehabilitation technologies. Signal-driven neuro-musculoskeletal models may offer a novel paradigm for devising novel closed-loop rehabilitation strategies according to an individual's physiology. However, current modelling techniques are constrained to bipolar electromyography (EMG), thereby lacking the resolution necessary to extract the activity of individual motor units (MUs) in vivo. In this work, we decoded MU spike trains from high-density (HD)-EMG to obtain relevant neural properties across multiple isometric plantar-dorsiflexion tasks. Then, we sampled MU statistical distributions and used them to reproduce MU specific activation profiles. Results showed bimodal distributions which may correspond to slow and fast MU populations. The estimated activation profiles showed a high degree of similarity to the reference torque (R-2 >0.8) across the recorded muscles. This suggests that the estimation of MU twitch properties is a crucial step for the translation of neural information into muscle force.