Background: Although traditional Chinese Yijinjing Qigong Exercise (YJJQE) is popularly used in China, to alleviate symptoms of people with knee osteoarthritis (KOA), no randomized controlled trials (RCTs) are available to evaluate the effects of YJJQE in patients with KOA. The purpose of this trial is to assess the clinical efficacy of YJJQE for patients with KOA.Methods: A total of 50 participants clinically diagnosed with KOA are randomly (1:1) assigned to the YJJQE group (n = 25) and to the stretching training exercise (STE) group (n = 25), for a 40-min exercise session twice a week for 12 weeks. All outcome measures are collected at baseline and at 12-week ending intervention, which includes the primary outcomes of Western Ontario and McMaster Universities Osteoarthritis Index Scale (WOMAC), the secondary outcomes of visual analog scale (VAS), mental component summary (MCS), physical component summary (PCS), Beck depression inventory (BDI), perceived stress scale (PSS), Berg balance scale (BBS), and the Gait functional mobility data.Results: The YJJQE group did not have any significant changes compared to the control group on the WOMAC score after the 12-week intervention (P > 0.05), though the YJJQE group demonstrated better performance in MCS, BDI, and PSS (P = 0.002, P = 0.001, and P = 0.026, respectively) than the control group. No serious adverse events occurred in either group, and only mild muscle soreness was reported during both exercise treatments.Conclusion: Because no difference between both groups was shown, with regards to the primary outcome measurement (WOMAC), it can hardly explain that the YJJQE had an advantageous effect on patients experiencing the pain and dysfunction of knee osteoarthritis. However, compared to the control group, YJJQE appeared to be associated with improvements in psychological well-being including reduced stress, anxiety, depression, and mood disturbance to manage KOA. Further trials with larger sample sizes and follow-up studies will be required.Clinical Trial Registration:https://www.chictr.org.cn/edit.aspx?pid=60357&htm=4, ChiCTR2000037256.

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A number of machines have been developed for the upper limb rehabilitation to meet patients' needs for upper limb rehabilitation exercises. As active exercise has proven to be effective and necessary for neural rehabilitation and motor recovery, it is suggested to be implemented to the rehabilitation machines. Toward this goal, the human motion desire should have been recognized exactly first. Because the muscle strength of the patient's upper limb may not be able to supply the gravity of the arm and the rehabilitation machines, some torques coming from the rehabilitation machines will be needed to supply the exercise. This paper is focused on modeling and identifying the human-exoskeleton interaction (HEI) dynamics, so that the motion desire is able to be recognized exactly based on it. First, the human arm can be taken as two links with three degrees of freedom (two DOF in shoulder, one in elbow). By combining the dynamics model of the human arm and the exoskeleton, the HEI dynamic model was formed. Meanwhile, the joint angles and torques of human arm can be measured indirectly by using the position and torque sensors mounted on the joints of the exoskeleton. In this way, a 19-parameter HEI dynamic model has been established using the Lagrange method based on the pseudo inertia matrix. The model can be used to estimate the torques during rehabilitation exercises after identifying the underdetermining dynamic parameters. The motion desire can be recognized by comparing the measured joint torques to the estimated ones. Finally, the feasibility of the HEI dynamic model is validated by simulation experiments.

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As the manufacturing of vast amount of information transmits at an unprecedented rate, traditional learning is no way to meet the needs of open learning and lifelong learning, how can distance learning be used to help us study become a problem to think about .Distance learning requires improvement of interactivity and initiative to achieve their aptitude. Using a variety of formats data, learners master the initiative in the learning process, it is not easy to lead to deviation from the learning goals phenomenon. To this end, in order to solve this problem, we use the basis of ILTMK theory and distributed interactive technology to introduce the active learning factors and propose a dynamic autonomous knowledge learning model and build its model's simulation running environment and related mechanism. Through simulation experiment, we can verify the effectiveness of the autonomous learning methods.