Humanoid Control Technology for Lower Limb Rehabilitation Robots Based on Human Gait Data

The lower limb rehabilitation robot is a wearable exoskeleton bionic device that integrates robotic features with human walking characteristics. This paper explores the control strategies, gait data acquisition and processing methods, as well as the design and experimental validation of a humanoid control system for lower limb rehabilitation robots. By collecting gait data from healthy individuals using the NOKOV 3D infrared passive optical motion capture system, a gait dataset was established, and methods such as spline interpolation and Gaussian regression were employed to integrate the gait data. A humanoid control system based on real gait data was designed, utilizing a high-performance computer, R4SE controller, and joint motors to simulate gait patterns. Experimental results demonstrate that the system effectively follows target gait trajectories, achieving high trajectory tracking accuracy and smoothness, ensuring the safety and effectiveness of rehabilitation training. Future research will incorporate a more diverse group of subjects and intelligent control algorithms to enhance the system’s adaptability and intelligence.