1. REHABILITATIVE STRATEGIES OF MULTIPLE LOWER LIMBS TRAINING MODELS
- Author
-
Ke-yi Wang, Wan-Li Wang, Zhuang Han, Zhao Wenyan, and Tang Xiaoqiang
- Subjects
0209 industrial biotechnology ,medicine.medical_specialty ,Rehabilitation ,Computer science ,medicine.medical_treatment ,020208 electrical & electronic engineering ,Biomedical Engineering ,Training (meteorology) ,02 engineering and technology ,body regions ,020901 industrial engineering & automation ,Physical medicine and rehabilitation ,0202 electrical engineering, electronic engineering, information engineering ,medicine ,Robot ,Torque ,Single point ,Joint (geology) ,Muscle force - Abstract
According to the combination of lower limbs rehabilitative robot (LLRR), the effect of multi-point and single point driving form on muscle force and joint torque is explored, and the rehabilitation effect of the training mode of the active–passive rehabilitation training is studied. The musculoskeletal model of lower limbs is established based on the physiological structure of human lower limbs. And considering the position of the attachment points of each muscle, the mechanical properties of muscles and applied moment of joints can be obtained under different rehabilitative training strategies by inverse dynamic analysis. The rehabilitation training strategies of flexion–extension and abduction and adduction movements are put forward according to the movement of lower limbs. And using the wire-driven rehabilitation robot as the driving device of the rehabilitation training, the robot is used to simulate the motor function of patients’ lower limbs by modifying the parameters of muscle which can affect the resistance moment of joint motion, then the effects of driving form and the active–passive training mode are analyzed. The results show that single point driving form is better than multi-point on muscle strength and joint strength training; the rehabilitation training strategies of flexion–extension and abduction–adduction movements show different superiority.
- Published
- 2018