The major drawbacks of current lower limb rehabilitation robots are high cost and complex structure which make them inappropriate to be applied in the community and family. In this paper, we design an 1-degree-of-freedom (DOF) robot with humanoid gait for lower limb rehabilitation based on Watt-I six-bar mechanism. Let the normal gait trajectory be target trajectory, the dimensions of the mechanism are calculated by path synthesis. First, the objective function to reflect the accuracy of trajectory reproduction and relevant constraints are established. Then GA-BFGS hybrid algorithm is used to minimize the objective function. After that, the optimized mechanism is analyzed by trajectory comparison, velocity / acceleration analysis and joint angle detection. Further, the kinematic simulation of the mechanism is also completed. The results show that while the crank is rotating at a constant speed, the mechanism can reproduce the time sequence and the shape of target trajectory approximately to realize walk training for patients with lower limb disorders whose legs are 810.0–860.0mm long (the corresponding heights are about 1650.0–1750.0mm). Finally, the specific structure of lower limb rehabilitation robot based on this mechanism is designed and the principle prototype model is given.
Traditional trajectory planning approaches are currently lacking in intelligence and autonomy. We used the reinforcement learning approach to solve the autonomous trajectory planning of the robot arm to avoid obstacles with uniform motion and hit the target point quickly with obstacle avoidance planning for surgical robots taken as the practical background. We used the algorithm of experience playback mechanism combined with off-policy DDPG based on reinforcement learning, and after several iterations, the robot completed trajectory planning with obstacle avoidance autonomously. Moving obstacles were added to roughly simulate the autonomous obstacle avoidance of a surgical robotic arm with moving medical personnel or mobile instruments in the operating room, based on the simple trajectory planning example of Open-AI Open-Source Project Baseline, combined with the research context. Sparse rewards were used for each iteration based on the HER algorithm, so that each attempt could gain experience. The HER-DDPG method can quickly complete the manipulator’s trajectory planning in a simulation environment, which is critical for the surgical robot’s autonomous positioning in the real world. Furthermore, the experience playback system has been tested to allow full use of sparse rewards and handle parallel tasks equally well.
Computer-assisted cognitive training is an effective intervention for patients with mild cognitive impairment (MCI), which can avoid the disadvantages of traditional cognitive training that consumes a lot of medical resources and is difficult to be standardized. However, many computer-assisted cognitive training systems have unfriendly human-computer interaction, for not considering that most MCI patients have certain difficulties in using computers. In this paper, we design a cognitive training system which allows patients to implement human-computer interaction through gestures. First, a gesture recognition algorithm is proposed, in which we implement gesture segmentation based on YCbCr color space and Otsu algorithm, extract Fourier Descriptors of gesture contour as feature vectors and use SVM algorithm to train a classifier to recognize gestures. Then, the graphical user interface (GUI) of the system is designed to realize the task requirement of cognitive training for the MCI patients. Finally, the results of tests show the accuracy of the algorithm and the feasibility of the GUI. With the above computer-assisted cognitive training system, patients can achieve human-computer interaction only through gestures without the need to use keyboard, mouse, etc., greatly reducing the burden of patients during training.
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