Nowadays, mechanical devices such as robots are widely adopted for limb rehabilitation. Due to the variety of human body parameters, the rehabilitation motion for different patient usually has its individual pattern. Thus it is obviously not an optimal solution to use a single motion generator to suit all patients. Yet it would also be unpractical if we design a different motion or even a different mechanism for each user individually. Therefore, in this paper we seek to 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. Firstly, the trajectory of a specified rehabilitation motion are recorded from various subjects, and then 4 types of machine learning algorithms (spectral clustering, hierarchical clustering, self-organizing mapping neural network and Gaussian mixture model) are implemented and compared. It is shown that spectral clustering (SC) yields the best performance and is hereby adopted to generate three clusters of motion patterns. After regression of each cluster, three types of motion for upper limb-rehabilitation are constructed, which could reflect the trajectories’ similarity and difference of people who have various body parameters. These work will provide help for the design of rehabilitation mechanisms.

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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A task motion trajectory usually needs to be determined for the training process and mechanism design for rehabilitation patients since they are not capable of providing a normal motion. In this paper, a machine-learning-based approach of gait trajectory prediction for lower limb rehab patients is proposed to provide the basis for the design of simple 1-degree-of-freedom (DOF) rehab mechanisms. First, a large amount of gait trajectories from various healthy volunteers are collected along with their body parameters, and a normalization method is presented to trim/expand these trajectory samples to a standard length and timing while retaining their shape and velocity information. Then, these 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-algorithm-optimized support vector machine method is adopted to train 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, it can predict a most suitable gait trajectory for the specific patient. It shows that the accuracy of trajectory prediction reaches 96% both on training set and test set which verifies the effectiveness of the method. In the end, a 1-DOF gait rehab mechanism design example is provided to illustrate the application of the proposed method. Taking the predicted result from the classifier as the task motion trajectory for the synthesis of mechanisms, a 1-DOF six-bar mechanism is designed and the patient-mechanism matching can be realized.