| description abstract | Mechanical devices such as robots are widely adopted for limb rehabilitation. Due to the variety of human body parameters, the rehabilitation motion for different patients usually has its individual pattern; hence, we adopt clustering-based machine learning technique to find a limited number of motion patterns for upper-limb rehabilitation, so that they could represent the large amount of those from people who have various body parameters. By using the regression motion of the clustering result as the target, in this article, we seek to apply kinematic mapping-based motion synthesis framework to design a 1-degree-of-freedom (DOF) mechanism, such that it could lead the patients’ upper limb through the target motion. Also, considering rehab training generally involves a large amount of repetition on a daily basis, this article has developed a rehab system with unity3d based on virtual reality (VR). The proposed device and system could provide an immersive experience to the users, as well as the rehab motion data to the administrative staff for evaluation of users’ status. The construction of the integrated system and the experimental trial of the prototype are presented at the end of this article. | |
