A upper limb rehabilitation system with motion intention detection

Abstract

Upper limb rehabilitation robotic systems have been studied for decades to improve the rehabilitation training of hemiplegic patients. Ideally such robotic systems should be capable of detecting the intentions of the patients and assisting them as needed. In this paper, we integrate a virtual environment and a low-cost motion sensor, the Microsoft Kinect II, into a robotic upper limb rehabilitation system to detect user motion intentions and generate motion commands for a rehabilitation robot. The system requires users to mimic a pre-programmed bimanual motion sequence shown in the virtual environment. A Gaussian Process based predictive controller uses the motion of the unaffected arm and the programmed motion sequence to estimate the motion intentions of the affected arm. We also adopt this controller in Mirror Therapy, a widely-practised therapeutic intervention method. Two preliminary experiments have been conducted to validate the proposed controller and test proper function and safety of the system including a patient case-study.