基于微控制器的智能穿戴设备在大学生体育锻造应用中的评估

Yong Che, Kaixuan Che, Qinlong Li
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摘要

简介:智能可穿戴设备在医疗保健和体育等各个领域的广泛使用,凸显了其在大学生体育锻炼中应用的重要性。最近技术的进步促进了评估和预测体育锻炼结果的复杂方法的发展,使其评估变得越来越重要:本研究旨在为大学生体育活动中使用的智能可穿戴设备开发一个可靠的评估模型。目的:本研究旨在为大学生体育活动中使用的智能可穿戴设备开发可靠的评估模型,以准确预测和评估这些设备在改善学生身体健康和促进终身体育习惯方面的效果。方法:研究引入了一种新型评估模型,该模型结合了基于斑马行为的启发式优化算法和卷积神经网络(CNN)。通过分析来自可穿戴设备的用户行为数据,该模型构建了一套针对大学生体育活动的评估指标体系。结果:该评价模型具有较高的准确性,在预测大学生体育锻炼结果方面有显著提高。与传统方法的对比分析表明,新模型减少了预测误差,提高了实时性能指标。具体而言,该模型在模拟测试中的均方根误差(RMSE)更低,表明评估更加精确。结论:所开发的评估模型极大地推动了智能可穿戴设备在监测和增强大学生体育活动方面的应用。通过整合尖端算法,该研究不仅提高了运动评估的准确性,还有助于人们更广泛地了解技术在健康和健身教育中的作用。未来的研究可以通过纳入更多的传感器和数据点来进一步完善这一模型,从而扩大其适用性和稳健性。
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Evaluation of a Microcontroller-based Smart Wearable Device in College Students' Sports Forging Application
INTRODUCTION: The widespread use of smart wearable devices in various fields, including healthcare and sports, underscores the importance of their application in enhancing physical exercise among college students. Recent advancements in technology have facilitated the development of sophisticated methods to assess and predict physical activity outcomes, making their evaluation increasingly critical.OBJECTIVES: This study aims to develop a reliable assessment model for smart wearable devices used in college students' sports activities. The objective is to accurately predict and evaluate the effectiveness of these devices in improving students' physical health and promoting lifelong sports habits. Ultimately, the research seeks to integrate advanced computational methods to enhance the accuracy of physical exercise assessments.METHODS: The research introduces a novel assessment model that combines a zebra behavior-based heuristic optimization algorithm with a convolutional neural network (CNN). By analyzing user behavior data from wearable devices, the model constructs an evaluation index system tailored for college sports activities. The approach optimizes the parameters of the CNN using the zebra optimization algorithm, ensuring enhanced prediction accuracy.RESULTS: The evaluation model demonstrated high accuracy, with a significant improvement in predicting the outcomes of physical exercises among college students. Comparative analyses with traditional methods revealed that the new model reduced prediction errors and increased real-time performance metrics. Specifically, the model achieved a lower root mean square error (RMSE) in simulation tests, indicating more precise assessments. Figures and statistical data provided in the study illustrate the model's superior performance across various parameters.CONCLUSION: The developed assessment model significantly advances the application of smart wearable devices in monitoring and enhancing college students' physical activities. By integrating cutting-edge algorithms, the study not only improves the accuracy of exercise assessments but also contributes to the broader understanding of technology's role in health and fitness education. Future research could further refine this model by incorporating additional sensors and data points to expand its applicability and robustness.
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