In order to improve the detection function of wearable intelligent devices in the Internet of things and facilitate people to control a variety of information such as heart rate, exercise state, blood oxygen saturation, and so on, the scientific detection of human physical health based on wearable devices based on Internet of things technology is proposed. Through the combination of software- and hardware-related functional modules, the real-time detection of human physical health information can be effectively realized. Firstly, the detection principle of optical capacitance product pulse wave signal and the waveform characteristics of pulse wave are introduced, and then the application scenarios and advantages of wearable devices are further introduced; then, the convolutional neural network for pulse wave signal denoising and the basic principle of self-encoder are introduced; finally, the regression prediction method and support vector machine method for pulse wave signal feature extraction are introduced in detail. The pulse wave based on optical capacitance product is removed to improve the waveform quality of pulse wave signal. Firstly, the system software development environment is briefly described. Then, the software design of watch terminal master device based on MSP432 and belt terminal slave device based on MSP430 are described in detail, and the detailed program implementation flow of each key technology in the system is given. In addition, the fall detection algorithm based on threshold discrimination is studied, and the program implementation of the algorithm is also described in detail. Finally, the system is tested. The results show that normal state mainly include normal walking, jogging, and fast sitting, and the accuracy rate is 97%, 95%, and 93%, respectively. For fall state, the experimenter needs to simulate various possible fall states, and the accuracy rate is 95%, 93%, and 95%, respectively, which verifies the detection accuracy of the algorithm. The system can automatically turn on the satellite positioning function when the user’s physical sign parameters are abnormal or the user’s current fall dangerous situation occurs, and send the current position information and alarm content information through the GSM module, so that the dangerous situation can be found and handled at the first time.

Purpose: This study focused on different exercise motivations, especially mood regulation and their relation to the possible influencing factors of adults in China.Methods: 5242 participants aged 20-69 years from 2016 to 2018 were recruited in this study to finish the questionnaire of Guangdong National Physique Monitoring organized by the Guangzhou Institute of Sports Science. Multiple statistical analyses methods were used to study each exercise motivation and its sociodemographic characteristics (gender, age, education and job), exercise measurements (frequency, duration and intensity) and physical conditions (BMI, abdominal obesity and basic diseases). An exercise index that is good for mental health (index 1: 45 min per session and 3-5 times per week; index 2: exercise motivation of mood regulation and exercise 60-120 min per week) was also used to investigate the number and type of people who were more likely to meet the index.Results: Substantial evidence showed that exercise is good for mood regulation, but 44.9% (2355/5242) of participants showed exercise inactivity in this study. Only older participants and those with an average level of education showed a significant association with mood regulation. Few people met the index that is good for mental health (16.64% (872/5242) met index 1 and 2.84% (149/5242) met index 2), and higher education showed a significant association with a reduction in the mental health burden and the prevention of depression.Conclusion: This investigation suggests motivating people to be more active, educating people on the mental health benefits of exercise.

Objective. This study focused on mood regulations and their association with sociodemographic status, exercise pattern, and physical conditions of adults and older adults in China who did not undergo interventions. Method. Data were based on the 2016 to 2018 Guangdong National Physique Monitoring data, in which 5242 participants aged 20-69 years were recruited. Multiple statistical analysis methods, such as descriptive and logistic regression analyses, were used to study each exercise motivation and its association with influencing factors, including sociodemographic characteristics, exercise measurements, and physical conditions. An exercise index for mental health was also used to investigate the number and types of people who were more likely to meet the index. Results. We observed that 44.9% (2355/5242) of participants did not engage in physical exercise in this study. Only older participants (40 to 69 years old) and those with an average level of education (high school/technical secondary school) showed a significant association with exercising for mood regulation. Few people met the index that is good for mental health (16.64% [872/5242] met index 1, and 2.84% (149/5242) met index 2), and higher education showed a significant association with a reduction in the mental health burden and the prevention of depression. Conclusion. This study found that motivating people to be more active and educating them on the potential mental health benefits of exercise could help them to exercise more.