We will use the simulation data for the optimal sFES muscle set to optimise the controller for the stimulation.
We will train an artificial neural network (ANN) to predict the sFES patterns from voluntary muscle activations that we can record using surface electromyography. If injury level is such that certain motions can be done independently (e.g. paralysis at the elbow and distally, but not at the shoulder), recorded kinematics will also be used as inputs to the ANN.
[ ] Separate voluntary muscle activations from sFES patterns
[ ] Create training, validating and testing data sets
We will use the simulation data for the optimal sFES muscle set to optimise the controller for the stimulation.
We will train an artificial neural network (ANN) to predict the sFES patterns from voluntary muscle activations that we can record using surface electromyography. If injury level is such that certain motions can be done independently (e.g. paralysis at the elbow and distally, but not at the shoulder), recorded kinematics will also be used as inputs to the ANN.