Application effect of rehabilitation robots in rehabilitation of limb movement disorders based on neural network algorithms

Abstract

With the continuous advancement of computer technology and sensor technology, rehabilitation robots have shown great potential in the rehabilitation treatment of limb movement disorders. This paper designs a rehabilitation robot based on a neural network algorithm to improve the rehabilitation effect of patients with limb movement disorders. The robot consists of a four-axis robotic arm, a three-finger gripper, a wheeled chassis and a variety of sensors. The control program written in C language and the host computer program written in Python language realize the control of motion and force, and the neural network algorithm is used to accurately adjust the position and force. In the control system design, the back propagation algorithm is used to train the neural network, and it is optimized in combination with multiple data to ensure that the robot can accurately track and assist the patient's limb movement. In order to verify the effect of the rehabilitation robot, this paper conducted an experimental evaluation. The experimental group was patients who received rehabilitation training based on the neural network algorithm, and the control group was patients who received traditional physical therapy. Through the evaluation of multiple indicators such as the 10-meter walking test, Berg balance scale, Jebsen-Taylor hand function test and MOS SF-36 health questionnaire, the experimental results showed that the improvement of the experimental group in each test was significantly better than that of the control group. Especially in the Berg balance scale and Jebsen-Taylor hand function test, the scores of the experimental group were significantly improved, indicating that the rehabilitation robot plays an important supporting role in the rehabilitation training of patients with limb movement disorders and has broad application prospects.