With the improvement of the quality of national life, the physical health problem is gradually placed in the same important position as the basic problems such as education, people’s livelihood, and safety. Improving the physical quality of college students and promoting the healthy growth of students are major events in the future of the country and the nation. Therefore, each school will conduct a physical test every year. The use of wireless sensors for data acquisition has played an important role in various fields. Therefore, for the physical health management of college students, this paper proposes to design a sensor system to collect physical measurement data. The measured data is collected through the sensor, such as the XGZP6847 gas pressure sensor to collect students’ vital capacity. Then, the sensor node and the coordinator are connected to the PC through the serial port, respectively, and the CC2530 chip transmits the data to the coordinator through the ZigBee module, establishes communication with the PC through the 2.4G wireless communication module, adopts the strategy of the thread pool to save resources, improves the response speed, and obtains students’ body test data in real time. Taking the data acquisition and transmission of vital capacity and body weight subjects as an example, the system realizes the accurate transmission of the collected test data from the sensor to the terminal, greatly improves the accuracy and real-time body test data acquisition, and reduces the probability of errors in the recording process, and the collected data can be used directly or for secondary development, and it promotes the establishment of college students’ physical health management system.

In order to solve the problems of inaccurate information collection, incomplete information collection, and inconsistency of collected images in traditional sports injury collection methods, an application method of moving target information perception technology in intelligent supervision system is proposed. By judging and analyzing the potential motion damage posture of the motion posture intelligent tracking images, the collected motion intelligence tracking images are judged. The intelligent tracking image matrix can make up for the shortcomings of traditional images that are not connected, complete the identification, detect potential damage in time, and take targeted preventive measures and means. Finally, according to the target detection algorithm and target tracking algorithm, combined with OpenCV computer vision library and QT image library, an intelligent video surveillance target tracking simulation system is developed. The algorithm studied in this paper is to realize the target tracking function of the intelligent video surveillance system. Through the comparison of experimental results, the design method can accurately collect damage attitude information, without calculating continuous values, and the use of three-dimensional images in the positioning process can analyze the damage attitude from multiple angles.

A growing amount of people are beginning to monitor themselves with the rapid emergence of a wide variety of cost-effective personal sensing instruments. To measure different facets of personal life, innovation helps people better understand their lifestyles, enhance their work quality, or maximize various health factors, allowing free-living. Although vast amounts of raw information on the provisioning and physiological parameters have been obtained much more straightforward, making use of all the information remains a significant task. The article introduces the Physical Activity Analysis Framework (PAAF) for the Elderly Person in Free-Living Conditions. In the framework, the acceleration signals split into overlapped windows and derive information in each frame’s frequency domain. The framework’s sensors sense the activity and evaluate a profound learning structure dependent on each window’s progressive networks. The proposed IoT model has multiple layers separately connected with each sensor, and the critical element integrates the outputs of all sensors for the classification of physical activity. In longer cycles, the model combines the window decision with a substantial increase in its efficiency. The model in the research has been evaluated using labelled free-living pilot data. Eventually, discover the use of the proposed models from a broader lifestyle intervention analysis in unlabeled, free-living data. The results show that the proposed model performs well for both labelled and unlabeled data. The experimental analyses of an older person in living conditions with their daily activities to be monitored via IoT system as Meditation effect analysis ratio is 86.6%, Physical activity ratio is 87.12%, Physical disability ratio is 87.1%, Exercise satisfaction ratio is 85.05%, and Self-efficacy ratio is 93.5%.