Lower Limb Exercise Rehabilitation Assessment Based on Artificial Intelligence and Medical Big Data

Abstract

This paper firstly compares the common virtual reality technology production methods, determines the reasonable lower limb rehabilitation exercise modeling method, establishes a more accurate human lower limb musculoskeletal rehabilitation posture mechanism model, analyzes the passive movement work mode of lower limb rehabilitation exercise, and simulates the changes of human musculoskeletal changes during passive movement of lower limb rehabilitation which exercise robots were analyzed. Secondly, the research is on robust controller for omni-directional mobile lower limb rehabilitation based on artificial intelligence and medical big data. The error dynamic model of omni-directional moving lower limb rehabilitation exercise system is established, and the technical problems of standard design, dissipative and gain are analyzed. By constructing the storage function and using the inverse push method, the nonlinear robust controller for omnidirectional moving lower limb rehabilitation motion is designed. The stability of this control law is proved based on Lyapunov's theorem. Finally, an experimental study on the omni-directional moving lower limb rehabilitation exercise system and rehabilitation evaluation system. Seven human gait and online detection methods for rehabilitation exercise were proposed. The simulation study on the omni-directional moving lower limb rehabilitation robot using nonlinear robust controller is carried out to verify the effectiveness and correctness of the lower limb exercise rehabilitation method.

Keywords

• Artificial intelligence
• medical big data
• lower limb exercise rehabilitation
• robust control