An electromyogram muscle strength prediction method suitable for hand function rehabilitation training of children with cerebral palsy

Abstract

The application provides an electromyogram muscle strength prediction method suitable for hand function rehabilitation training of children with cerebral palsy, and specifically comprises the following steps: constructing a muscle strength prediction source domain network model, and using an electromyogram muscle strength database to train and test the model; proposing a gesture set and a synchronous acquisition scheme of electromyogram signals and muscle strength signals of each gesture according to the rehabilitation training requirements of children with cerebral palsy; collecting electromyogram and muscle strength data generated when healthy children perform each gesture action, optimizing the muscle strength prediction source domain network model by using a transfer learning method, and obtaining a specific muscle strength prediction model for each gesture; collecting electromyogram signals and muscle strength signals of children with cerebral palsy during rehabilitation training of different gestures, realizing muscle strength prediction by using the specific muscle strength prediction model, and evaluating the completion effect of rehabilitation gestures of children with cerebral palsy according to the error between the predicted value and the actual value. The method is conducive to formulating a specific rehabilitation scheme and evaluating the rehabilitation treatment effect by clinicians, and better promotes the rehabilitation treatment of children with cerebral palsy.

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