Abstract This article studies the rehabilitation capture analysis method for limb motion training based on human motion capture. This method aims to improve the effectiveness of exercise training and the quality of rehabilitation by capturing the motion characteristics of the human body and conducting training and rehabilitation analysis on limb movements. On this basis, this article proposes a limb motion rehabilitation analysis method based on... This article studies the rehabilitation capture analysis method for limb motion training based on human motion capture. This method aims to improve the effectiveness of exercise training and the quality of rehabilitation by capturing the motion characteristics of the human body and conducting training and rehabilitation analysis on limb movements. On this basis, this article proposes a limb motion rehabilitation analysis method based on fuzzy decision trees. This method utilizes fuzzy set theory and decision tree algorithm to evaluate and predict the rehabilitation effect of limb movements. Specifically, a series of fuzzy rules are first set based on experience, and then the membership degrees of each rule are calculated based on the collected motion data. Finally, a decision tree model reflecting the rehabilitation effect is constructed using the decision tree algorithm. The experimental results indicate that the rehabilitation capture analysis method for limb motion training based on human motion capture can accurately evaluate and predict the rehabilitation effect of limbs, providing strong support for exercise training and rehabilitation treatment. Read More Read Less

Abstract With the rapid development of science and technology, the internet and new media have entered the era of algorithmic recommendation. This article investigates and analyzes the new characteristics of content dissemination and discourse expression in university network education from the perspective of recommendation algorithms. Addressing the practical dilemmas of value bias at different stages of integration, decreased independent thinking ability of learners, and divergence... With the rapid development of science and technology, the internet and new media have entered the era of algorithmic recommendation. This article investigates and analyzes the new characteristics of content dissemination and discourse expression in university network education from the perspective of recommendation algorithms. Addressing the practical dilemmas of value bias at different stages of integration, decreased independent thinking ability of learners, and divergence of social values, this study explores the incorporation of algorithmic recommendation techniques into university network education. It proposes guiding strategies, such as the “guiding algorithm,” “approaching algorithm,” and “moving away from algorithm,” to provide references for the realization of “recommendation algorithm + university network education” and promote the healthy development of university network education. Read More Read Less

Abstract Physical exercise is essential for the sustainable self-cultivation of college students and is an indispensable part of higher education. However, most college students lack systematic knowledge of physical exercise, making it challenging to exercise scientifically. Therefore, this paper introduces the ant colony algorithm into the model for extracting characteristics of college student's physical exercise behavior to improve the effectiveness of... Physical exercise is essential for the sustainable self-cultivation of college students and is an indispensable part of higher education. However, most college students lack systematic knowledge of physical exercise, making it challenging to exercise scientifically. Therefore, this paper introduces the ant colony algorithm into the model for extracting characteristics of college student's physical exercise behavior to improve the effectiveness of behavior recognition and clustering. Furthermore, the model's performance is enhanced through optimization of the ant colony algorithm. The experimental results demonstrate that the clustering model of college student's physical exercise behavior, based on the ant colony algorithm, effectively reduces the error rate and maintains good accuracy as the sample size increases, indicating good stability and reliability. Additionally, for different physical exercise behaviors, the clustering F-measure standard values of the model all exceed 0.8, indicating a better clustering effect. Read More Read Less