The monitoring effect of the system is verified by comparing with the related work in recent years. Reference designs an autonomous fitness monitoring system based on a monocular camera, which relies on the monocular camera of a mobile phone and 3D human key point detection technology to monitor whether the fitness actions of users are standard in real time and give corresponding prompts through voice. In order to more conveniently manage and utilize the research results in the field of sports science such as national physique monitoring, promote the transformation of the research results of physique monitoring into practical application results, and facilitate individuals to get the feedback and scientific guidance of physique test results in time, it is urgent to establish a sports physical fitness monitoring system. At the same time, how to use the data of national physique monitoring to scientifically guide the national fitness and apply the national physique monitoring results to practice is also the focus and development direction of physique monitoring research. Moreover, due to the mobility of the population, it brings great inconvenience to large-scale and effective physique monitoring, longitudinal tracking research, and personal understanding of their own physique. However, at present, the physique data databases established by various units are isolated from each other. With the application of computer-related technology in the field of sports, sports-related units at all levels have established their own sports information management or sports physical fitness monitoring management systems to systematically manage relevant data. The experimental results show that the monitoring results of the proposed system have higher accuracy, faster system response speed, and higher comprehensiveness of the monitoring results, which verifies the application value of the proposed system. Based on the monitoring data, the fitness method recommendation method is designed according to the user's physical condition. The ray-casting image feature scanning method was used to collect the physical condition monitoring image and generate a high-quality human body target frame to realize the physical condition monitoring. Based on cloud storage technology, the overall framework of the sports fitness monitoring system is established the function of the hardware module of the monitoring system is analyzed, and distributed database is established. In order to improve the accuracy and response speed of sports fitness monitoring results and make the monitoring results more comprehensive, a new cloud storage-oriented sports fitness monitoring system is designed.
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