In response to the current problem of low intelligence of mobile lower limb motor rehabilitation aids. This paper proposes an intelligent control scheme based on human movement behavior in order to control the rehabilitation robot to follow the patient’s movement. Firstly, a multi-sensor data acquisition system is designed according to the rehabilitation needs of the patient and the movement characteristics of the human body. A mathematical model of movement behavior is then established. By analyzing and processing motion data, the change in the center of gravity of the human body and the behavior intention signal are derived and used as a control command for the robot to follow the human body’s movement. Secondly, in order to improve the control effect of rehabilitation robot following human motion, an adaptive radial basis function neural network sliding mode controller (ARBFNNSMC) is designed based on the robot dynamic model. The adaptive adjustment of switching gain coefficient is performed by radial basis function neural network. The controller can overcome the influence caused by the change of robot control system parameters due to the fluctuation of the center of gravity of human body, enhance the adaptability of the system to other disturbance factors, and improve the accuracy of following human body motion. Finally, the motion following experiment of the rehabilitation robot is performed. The experimental results show that the robot can recognize the motion intention of human body and perform the training goal of following different subjects to complete straight lines and curves. The correctness of human motion behavior model and robot control algorithm is verified, which shows the feasibility of the intelligent control method proposed in this paper.

In response to the rehabilitation needs of stroke patients who are unable to benefit from conventional rehabilitation due to the COVID-19 epidemic, this paper designs a robot that combines on-site and telerehabilitation. The objective is to assist the patient in walking. We design the electromechanical system with a gantry mechanism, body-weight support system, information feedback system, and man-machine interactive control system. The proposed rehabilitation robot remote system is based on the client/server (C/S) network framework to realize the remote control of the robot state logic and the transmission of patient training data. Based on the proposed system, doctors can set or adjust the training modes and control the parameters of the robot and guide remote patient rehabilitation training through video communication. The robotic system can further store and manage the rehabilitation data of the patient during training. Experiments show the human-computer interaction system of the lower limb rehabilitation robot has good performance, can accurately recognize the information of human motion posture, and achieve the goal of actively the following motion. Experiments confirm the feasibility of the proposed design, the information management of stroke patients, and the efficiency of rehabilitation training. The proposed system can reduce the workload of the doctors in practical training.

In this paper, a lower limb rehabilitation robot, suitable for stroke patients, is designed to meet the needs of the lower limb training in a later stage of rehabilitation. The rehabilitation robot is composed of a gantry structure, a driving system,...

Background: In response to the current problem of low intelligence of mobile lower limb motor rehabilitation aids, this article proposes an intelligent control scheme based on human movement behavior in order to control the rehabilitation robot to follow the patient's movement. Methods: Firstly, a multi-sensor data acquisition system is designed according to the motion characteristics of human body. By analyzing and processing the motion data, the change law of human center of gravity and behavior intention are obtained, and the behavior intention of human is used as the control command of the robot following motion. In order to achieve the goal of the rehabilitation robot following human motion, an adaptive radial basis function neural network (ARBFNN) sliding mode controller is designed based on the robot dynamic model. The controller can reduce the impact of fluctuations in the human center of gravity on changes in the parameters of the robot control system, and enhance the adaptability of the system to other disturbance factors, and improve the accuracy of following human motion. Finally, the motion following experiment of the rehabilitation robot is carried out. Results: The experimental results show that the robot can recognize the motion intention of human body, and achieve the training goal of following different subjects to complete straight lines and curves. Conclusions: According to the experimental results, the accuracy of the multi-sensor data acquisition system and control algorithm design is verified, which demonstrates the feasibility of the proposed intelligent control scheme.