Objective:To explore the application effect of lower limb strength training based on "Internet +" in the home rehabilitation of knee osteoarthritis (KOA) patients.Methods:Using the convenient sampling method, a total of 80 KOA patients who visited Pain Outpatient Department of Beijing Friendship Hospital, Capital Medical University from May 2021 to December 2022 were selected. They were randomly divided into the observation group and the control group by the random number table method, with 40 cases in each group. The control group adopted routine home rehabilitation nursing, and the observation group implemented lower limb strength training based on "Internet +" on the basis of the control group. Both groups were intervened for 3 months, and the knee joint pain degree (VAS score), knee joint function (KOOS score) and exercise compliance of the two groups were compared.Results:After intervention, the VAS score of the observation group was lower than that of the control group and the KOOS score was higher than that of the control group, and the differences were statistically significant ( P<0.05). There were 26 cases of high compliance, 11 cases of partial compliance and 3 cases of low compliance in the observation group, and 19, 10 and 11 cases in the control group, respectively. The exercise compliance of the observation group was better than that of the control group, and the difference was statistically significant ( Z=1.997, P=0.046) . Conclusions:The implementation of lower limb strength training rehabilitation management based on "Internet +" can effectively alleviate knee pain in knee osteoarthritis patients, improve knee function, and improve their compliance with home exercise.

In view of the high incidence of malignant diseases such as malignant arrhythmias in the elderly population, accidental injuries such as falls, and the problem of no witnesses when danger occurs, the study developed a human vital signs and body posture monitoring and positioning alarm system. Through the collection and analysis of electrocardiogram (ECG), respiration (RESP) and acceleration (ACC) signals, the system monitors human vital signs and body posture in real time, automatically judges critical states such as malignant arrhythmias and accidental falls on the local device side, and then issues alarm information, opens the positioning function, and uploads physiological information and patient location information through 4G communication. Experiments have shown that the system can accurately determine the occurrence of ventricular fibrillation and falls, and issue position and alarm information.
Humans ; Aged ; Arrhythmias, Cardiac/diagnosis* ; Ventricular Fibrillation ; Electrocardiography ; Accidental Falls ; Vital Signs ; Posture ; Monitoring, Physiologic