PROJECT TITLE :

Neural Data-Driven Musculoskeletal Modeling for Personalized Neurorehabilitation Technologies

ABSTRACT:

Objectives: The development of neurorehabilitation technologies needs the profound understanding of the mechanisms underlying a private's motor ability and impairment. A significant issue limiting this understanding is the problem of bridging between events happening at the neurophysiologic level (i.e., motor neuron firings) with those emerging at the musculoskeletal level (i.e. joint actuation), in vivo in the intact moving human. This review presents rising model-based methodologies for filling this gap that are promising for developing clinically viable technologies. Strategies: We offer a design overview of musculoskeletal modeling formulations driven by recordings of neuromuscular activity with a important comparison to alternative model-free approaches in the context of neurorehabilitation technologies. We tend to gift advanced electromyography-primarily based techniques for interfacing with the human nervous system and model-primarily based techniques for translating the extracted neural information into estimates of motor function. Results: We have a tendency to introduce representative application areas where modeling is relevant for accessing neuromuscular variables that might not be measured experimentally. We then show how these variables are used for designing personalised rehabilitation interventions, biologically galvanized limbs, and human–machine interfaces. Conclusion: The ability of using electrophysiological recordings to tell biomechanical models enables accessing a broader range of neuromechanical variables than analyzing electrophysiological knowledge or movement knowledge individually. This permits understanding the neuromechanical interplay underlying in vivo movement perform, pathology, and robot-assisted motor recovery. Significance: Filling the gap between our understandings of movement neural and mechanical functions is central for addressing one in every of - he major challenges in neurorehabilitation: personalizing current technologies and interventions to a private's anatomy and impairment.