|Place of Conferral||北京|
|Keyword||下肢康复训练 运动分析 轨迹规划 人机交互 协作控制|
With the problem of aging society becoming more and more serious, various disabled and long-term bedridden patients abound and continue to grow, the medical rehabilitation work is faced with unprecedented pressure, and to help the disabled and elderly is becoming a major social problem. Body-weight-support rehabilitation robot for lower limbs is designed for people with lower limb dysfunction, assisting patients to perform physical rehabilitation sessions. Body-weight-support rehabilitation robot can provide better rehabilitation treatment for the elderly and the disabled, reducing the work load of therapists, improving the quality of life of the elderly and the disabled after rehabilitation and has an important practical significance for reducing the burden of the society. In this paper, the key technologies of the body-weight-support rehabilitation robot are discussed, and the validity and reliability of the proposed method are verified by experiments. The main work and innovation of this paper are as follows:
Firstly, vision-based human lower limb movement analysis is proposed. With infrared markers arranged on the surface of human lower limb, the identification algorithm of lower limb inferred marker is proposed, and the method of calculating the position and posture of human lower limb via binocular stereo vision is utilized. The proposed method is implemented on an image processing board with FPGA and DSP as main calculation units. And the trajectory of the lower limbs of healthy individuals is captured on treadmill. Through the analysis of the trajectory of the lower limbs of healthy individuals, the model of lower limb joint movement was established, and the collected data were analyzed by the lower limb movement model. The trajectories of the lower limbs of the healthy individuals were collected and applied to the trajectory planning of the lower limb rehabilitation training robot.
Secondly, the passive training methods for patients is proposed and implemented. In view of the characteristics of the patients with lower ability of independent movement in the early stage of rehabilitation recovery, the passive training method is utilized. Given the poor adaptability of a single gait trajectory to different patients, this paper presents a parametric generation method for training trajectories. Based on the database of lower limb movement of healthy individuals, a neural network based motion trajectory generation algorithm is implemented, which can effectively improve the matching degree between robot training trajectory and natural gait and enhance the adaptability of training trajectory to patients. By analyzing the kinematic model of the lower limbs of the lower limbs of the robot, the rehabilitation training of the rehabilitation robot was carried out according to the trajectory of the lower limb generated by the physiological parameters of the current patient.
Thirdly, the active training methods for patients is proposed and implemented. In the middle or late stages of rehabilitation, patients have a certain degree of autonomy movement ability; the active rehabilitation training method is achieved via the robot and patient collaborative control method. Through the force sensing signal between the robot and the patient, the active motion intention of the patient is obtained based on the dynamic model of robot-patient hybrid system. According to the patient's initiative intention, to achieve the initiative based on the patient's control method, and successfully applied to the rehabilitation of the robot. For the patients with unilateral limb disability, a method of training for unilateral limb disability based on the trajectory of the healthy side of the patient was proposed. During the training，the patients can learn from their own movement，improving the condition of patients after bilateral gait incongruity problems. The feasibility and reliability of the patient-in-charge method is verified by experiments.
Finally, the summarization of the work is given and the further work is proposed.
|First Author Affilication||Institute of Automation, Chinese Academy of Sciences|
|王晓楠. 下肢康复训练机器人关键技术研究[D]. 北京. 中国科学院大学,2017.|
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