Upper limb rehabilitation requires long-term, repetitive rehabilitation training and assessment, and many patients cannot pay for expensive medical fees in the hospital for so long time. It is necessary to design an effective, low cost, and reasonable home rehabilitation and evaluation system. In this paper, we developed a novel home based multi-scene upper limb rehabilitation training and evaluation system (HomeRehabMaster) for post-stroke patients. Based on the Kinect sensor and posture sensor, multi-sensors fusion method was used to track the motion of the patients. Multiple virtual scenes were designed to encourage rehabilitation training of upper limbs and trunk. A rehabilitation evaluation method integrating Fugl-meyer assessment (FMA) scale and upper limb reachable workspace relative surface area (RSA) was
proposed, and a FMA-RSA assessment model was established to assess upper limb motor function.
Correlation based dynamic time warping (CBDTW) was used to solve the problem of inconsistent upper limb movement path in different patients. Two clinical trials were conducted. The experimental results show that the system is very friendly to the subjects, the rehabilitation assessment results by this system are highly correlated with the therapist’s (the highest forecast accuracy was 92.7% in the 13th item), and longterm rehabilitation training can improve the upper limb motor function of the patients statistically significant (p=0.02<0.05). The system has the potential to become an effective home rehabilitation training and evaluation system.[…]
Full Text PDF —> IEEE Xplore Full-Text PDF: