Intention Detection In Upper Limb Kinematics Rehabilitation Using A Gp-Based Control Strategy

In robot-assisted upper limb rehabilitation, detecting the intentions of hemiplegic patients is essential towards assisting the patients to actively exercise instead of driving passive motions. Many interactive channels, such as voice, EMG and EEG, have been studied to estimate the motion intentions. However, limitations of these techniques, such as high complexity, have constrained their applications in practice. In this paper, we integrate a virtual environment and a low-cost motion sensor into a novel control strategy to detect motion intentions for a rehabilitation robot. Several bimanual motion sequences are intuitively programmed by a professional therapist for subjects to repeat. The strategy uses the unaffected arm and the programmed motion sequence to estimate the motion intentions of the affected arm. We adopt this strategy in Mirror Therapy, a widely-practised therapeutic intervention method. Experiments have been conducted to validate the control strategy.