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.
Highlights
The combination of emerging artificial intelligence and medical big data technology and traditional medical and health fields will bring new opportunities to traditional medical models, help patients to develop medical solutions and medical institutions to integrate medical resources in the process of medical treatment
Disadvantages include: fewer types of training actions, the scope of motion is mainly limited to training the front of the body; the range of motion is small, generally limited to plane motion; the control strategy is single, mainly based on the speed or position servo control mode to provide passive training for patients, patients could not exercise independently; Rehabilitation evaluation indicators still use traditional clinical evaluation methods, so the relationship between the data extracted during the training process and the training effect is still unclear
The research is on robust controller for omni-directional mobile lower limb rehabilitation which based on artificial intelligence and medical big data
Summary
The combination of emerging artificial intelligence and medical big data technology and traditional medical and health fields will bring new opportunities to traditional medical models, help patients to develop medical solutions and medical institutions to integrate medical resources in the process of medical treatment. W. Ling et al.: Lower Limb Exercise Rehabilitation Assessment Based on Artificial Intelligence and Medical Big Data. The research is on robust controller for omni-directional mobile lower limb rehabilitation which based on artificial intelligence and medical big data. ANALYSIS OF LOWER LIMB EXERCISE REHABILITATION BASED ON MEDICAL BIG DATA A. W. Ling et al.: Lower Limb Exercise Rehabilitation Assessment Based on Artificial Intelligence and Medical Big Data TABLE 1. B. PASSIVE MOTION ANALYSIS OF LOWER LIMB EXERCISE REHABILITATION BASED ON MEDICAL BIG DATA Lower limb exercise rehabilitation In passive exercise, the simulated joint movement of the foot during the flexion and extension of the leg, the hip joint and the knee joint can be exercised during the flexion and extension of the large and small legs.
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