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, thus 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. Using the regression motion of the clustering result as the target, in this paper we seek to apply kinematic-mapping-based motion synthesis framework to design a one-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 in daily basis, this paper 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 as well as the experimental trial of the prototype are presented in the end of this paper.

For patients who need lower-limb kinetism rehabilitation training, this paper proposes an effective datadriven approach seeking the design of 1-degree-of-freedom (DOF) six-bar rehab mechanism through gait prediction by body parameters. First, gait trajectories from 79 healthy volunteers are collected along with their body parameters. Then, the normalized gait samples are clustered and regressed into a limited number of representative trajectories with K-means algorithm, and the cluster index is recorded as the label for each trajectory. Next, a genetic-algorithmoptimized support vector machine method is adopted to establish a classifier for the trajectories, obtaining the correspondence between body parameters and cluster labels of gait trajectories. As a result, once a group of body parameters are input into the classifier, the suitable gait trajectory can be predicted for the specific patient. A GA-BFGS algorithm is developed for 1-DOF six-bar mechanism synthesis and a GUI design software is presented that shows how the data-driven design process is realized. The novelty of this paper is using clustering and prediction technique to accomplish the patient-mechanism matching, so that simple, low-priced 1-DOF mechanisms could be adopted for large number of various patients without expensive customized design for each individual. In the end, a gait rehab device design example is provided, and a prototype device driven by a constant speed motor is presented, which illustrates the feasibility of the proposed method.

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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, thus 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. Using the regression motion of the clustering result as the task motion, in this paper we seek to apply kinematic-mapping-based motion synthesis framework to design a one-DOF mechanism such that it could lead the patients’ upper limb through the task motion. Also,considering rehab training generally involves a large amount of repetition in daily basis, this paper has developed an immersive rehab system with Unity3D based on Virtual Reality (VR). A patient user interface as well as an administrator user interface are presented, and a two-mode rehabilitation strategy is proposed. The construction of the integrated system and a prototype of the upper limb rehab device are also shown in the end of this paper.