One of the challenging issues in computational rehabilitation is that there is a large variety of patient situations depending on the type of neurological disorder. To improve the performance of motor neuroprosthetics beyond the current limited use of such system, subject-specific modelling would be essential. In addition, human characteristics are basically time variant, for instance, neuromuscular dynamics may vary according to muscle fatigue.

In order to correspond to time-varying characteristics, we believe that robust bio-signal processing and model-based control which can manage the nonlinearity and time variance of the system, would bring break-through and new modality in rehabilitation. In order to predict FES-induced joint torque, evoked-Electromyography (eEMG) has been applied to correlate muscle electrical and mechanical activities.

The robustness of the torque prediction has been investigated in a fatigue tracking task in experiment with Spinal Cord Injured subjects. The results demonstrate good tracking performance of muscle variations in the presence of fatigue and against some other disturbances. A new control strategy, EMG-Feedback Predictive Control (EFPC), was proposed to adaptively control stimulation pattern compensating to time-varying muscle state changes. It is implemented together with wireless portable stimulator.

In addition, Synergetic Learning Control is introduced for solving redundancy coordination issues in peripheral motor control. It is combined with BCI application for multi-DOF robot control with non-invasive brain signals.