Abstract
College students’ sports behavior is affected by many factors, and sports learning interest and sports autonomy support are potential psychological characteristic factors, which have ...important influence value on college students’ sports behavior. Machine learning methods are widely used to construct prediction models and show high efficiency. In order to understand the impact of sports learning interest and sports autonomy support on college students’ sports behavior (physical exercise level), the research decided to use the relevant methods of machine learning to build a prediction model, so as to find the internal relationship between them. This paper summarizes the relevant factors that affect college students’ sports behavior (physical exercise level) from two aspects, namely, sports autonomy and sports learning interest, and surveys the demographic and sociological information of college students as a supplement. The research evaluates the level of the prediction model through the construction of the prediction model of the machine learning algorithm and the comparison method, so as to determine the optimal prediction model. The results show that the prediction accuracy of the logistic regression model is 0.7288, the recall rate is 0.7590, and F1 is 0.7397; The prediction accuracy of KNN model is 0.6895, the recall rate is 0.7596, and F1 is 0.7096; The prediction accuracy of naive Bayesian model is 0.7166, the recall rate is 0.6703, and F1 is 0.6864; the prediction accuracy of LDA model is 0.7263, the recall rate is 0.7290, and F1 is 0.7265; The prediction accuracy of the support vector machine model is 0.6563, the recall rate is 0.7700, and F1 is 0.6845; The prediction accuracy of GBDT model is 0.6953, the recall rate is 0.7039, and the F1 score is 0.6989; The prediction accuracy of the decision tree model is 0.6872, the recall rate is 0.6507, and F1 is 0.6672. The logistic regression model performs best in the combination of sports learning interest and motor autonomy support, due to the combination of its linear classification characteristics, better adaptability, high computational efficiency, and better adaptability to feature selection and outlier processing. The conclusion points out that the prediction level of logistic regression model is the highest when combining sports learning interest and sports autonomy support to predict college students’ sports behavior (sports exercise grade), which also provides an important reference for improving college students’ sports behavior (sports exercise grade).
This study introduces an innovative electrochemical sensor leveraging copper oxide nanoparticles for detecting uric acid levels in athletes with enhanced sensitivity and selectivity. Employing a ...novel synthesis protocol, uniform copper oxide nanoparticles were integrated into a chitosan-glutathione biogel matrix, creating a high-performance sensing electrode. The nanoparticles were extensively characterized using XRD, SEM, FTIR, and XPS techniques, confirming their crystalline structure, morphology, and purity. The average crystallite size was determined to be 18 nm. Electrochemical evaluation of the sensor using cyclic voltammetry and electrochemical impedance spectroscopy revealed significantly improved electron transfer kinetics and catalytic activity towards uric acid oxidation. The sensor exhibited a wide linear detection range from 1 μM to 1.2 mM, encompassing both physiological and pathological uric acid levels. Notably, the limit of detection was found to be 0.27 μM, surpassing the performance of most existing metal oxide based uric acid sensors. The sensor also demonstrated excellent anti-interference capability against commonly coexisting species in biological fluids. Furthermore, the practical applicability of the sensor was validated by successfully determining uric acid concentrations in human serum, plasma, and sweat samples, with impressive recovery rates ranging from 96% to 102%. The stability, reproducibility, and cost-effectiveness of the developed sensor make it a promising tool for routine health monitoring and early intervention in athletes, facilitating personalized training and preventing complications associated with uric acid imbalances. The present work offers a significant advancement in non-invasive, reliable, and efficient assessment of uric acid, with potential for widespread implementation in sports medicine and general healthcare.
In order to solve the problem that multimedia technology cannot give full play to its technical advantages, a method of assisted teaching in hurdle technology based on mobile communication multimedia ...and MVP theory is proposed. In this mode, the system adopts a layered structure design, which can reduce the coupling between modules, realize the reuse of platform business logic code, and improve the maintainability of the entire system. The average technical evaluation score of the experimental class was 89.28, and that of the control class was 80.64. Meanwhile, the standard deviation was lower than that of the control class. A lower standard deviation indicates that the sample data is less distributed and that the population value is closer to the mean. That is to say, the technical evaluation scores of the experimental class were generally in the middle, and there was a significant difference between the two classes (P<0.05). The results of the pilot study showed that with the aid of mobile communication multimedia teaching technology, students’ interest in preventing technology learning is increasing, verifying the validity of the experiment.
Traditional methods overly rely on static or linear models, which cannot fully consider the dynamic and nonlinear relationships, resulting in inaccurate prediction of lower limb joint motion. This ...article combines the nonlinear modeling ability of neural networks and the fuzzy reasoning ability of fuzzy logic to improve the prediction performance of lower limb joint motion by better adapting to dynamic and nonlinear relationships. Firstly, this article collects lower limb motion data through various sensors, preprocesses the collected data for feature extraction, and uses Gaussian function for feature blur. It performs fuzzy inference through the combination of fuzzy rules, and finally outputs the results of lower limb joint motion prediction through ambiguity. A self-adaptive neuro fuzzy inference system is proposed to improve the predictive performance of lower limb joint motion by combining neural networks and fuzzy logic. ANFIS performs excellently in predicting lower limb joint movement, with an average mean square error of only 0.010, providing strong support for objective evaluation of movement disorders and disease diagnosis and treatment. This innovative method has made significant contributions to the fields of biomechanics and rehabilitation.
This paper uses three-dimensional video to analyze the three-point shooting movements of several basketball players, and establishes the relationship between the shooting angle, the shooting angle ...and the height of the parabola formed by the running trajectory of the three-point shooting. Established a database of three-point shooting angle, entry angle, and height of parabola, and developed three-point shooting training software, which has a basis and great help for basketball players' three-point shooting scientific training, which can greatly improve the hit rate.
Based on computer big data technology, this paper analyzes the relative peak torque, relative work, and replacement of knee joints of 12 swimming team athletes at three test speeds of 60°/s, 120°/s, ...and 180°/s in isokinetic flexion and extension. A mechanical study of air time and stroke speed. The study found that the relative peak torque and relative work of knee extensors and flexors in swimmers decreased with the increase of contraction speed. At the same time, a swimming propulsion tester is developed in this paper, and the design principle, main technical route and performance of the instrument, as well as the actual test and evaluation mode are theoretically analyzed.
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