At present, the physical condition of college students is declining day by day, and the relevant sports departments pay more and more attention to the daily physical exercise of college students. To deeply understand the physical health status of college students and further adjust the health intervention measures, this paper constructs a neural network reliability evaluation model and carries out the physical health standard test on 50 college students from the sports department of Nanchang University and analyzes the physical health test data by using the neural network error reverse evaluation method; to optimize the physical health test system, the accuracy of physical health test is fitted in parallel. Then, according to the fitting degree of the test results, the direction of health intervention is predicted, and some specific suggestions are made for college students’ health indicators such as running and vital capacity. The research shows that the fitting degree of the neural network reliability evaluation model is 86%, and the accuracy of the neural network model is high. In the 50 college students’ physical health project, the fitting degree of 50 m running and vital capacity is higher than that of long distance running. Therefore, the neural network reliability evaluation model is feasible for college students’ physical health tests and can rank the intervention items, which has great practical significance for the improvement of college students’ physical health.
Basketball is one of the most popular sports, but apart from a small number of sports specialties, ordinary people rarely have the opportunity to receive professional basketball training, let alone coaches who provide one-on-one dribbling posture guidance. Dribbling is a very basic and important technique in basketball. Mastering the correct dribbling posture can help people further improve their basketball skills. In response to this problem, this article designed a smart wearable product to monitor the user’s posture in basketball dribbling training. If the user has a wrong dribble posture, the product will automatically prompt and give relevant suggestions. This article focuses on user demand research, product conceptual design, prototype development, and dribbling posture determination experiments and elaborates the design and development process of the product. Based on the experimental data, this article believes that the optimal parameters of the monitoring standard for the “head down too long” during basketball dribbling are the x -axis angle value of the motion sensor is critical for -120°, and the duration of exceeding the critical value is 1 second. The optimal parameters of the “dribbling wrist flip” monitoring standard are the x -axis angle value of the motion sensor has a critical value of 105°, and the duration of exceeding the critical value is 0.4 seconds. Judging from the end user’s experience and rating of the product’s trial experience, it can be seen that the smart product can indeed play a very good auxiliary effect in the field of dribbling posture monitoring. Popularizing it in the daily training of basketball players can effectively promote the full informatization and intelligent development of sports.
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