Movement performance: a constructed multifactorial solution in persons with disability

A non-disabled person can choose among a wide selection of possible movement patterns, based on the context and goals; one can adopt a movement solution that prioritizes speed, another for adapting to icy sidewalks, and yet another for comfort.

In lieu of extraordinary priorities, the brain in non-disabled persons spontaneously chooses a movement pattern based on least exertion, with a fair amount of flexibility to accommodate individual musculoskeletal dimension and function.

For persons with motion disorders, however, the selection of movement patterns is vastly limited; muscle weakness, restricted joint mobility, muscle dysfunction, pain, and balance disturbance will affect motor performance, but precisely how much has never been quantified.

This project aims to establish the link between the motor, sensory and perception disorders and the resulting motor performance in persons with different motor disorders.

The strategies in this proposal involve a combination of predictive simulation, artificial intelligence, and experimentation.By combining a forward dynamic simulation approach  and a data-driven explainable machine learning approach with "true" data from experiments conducted on persons with motor disorders, we will construct models that quantify the multifactorial nature of human movement performance.

This predictive approach wil enable establishing fundamental natural links between motor symptoms, sensory function, and performance.