优化体育训练计划,以获得长期健康益处
Liang Tan1, Qin Chen2, Jianwei Wu1
1School of Sport and Health, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
Frontiers in public health
|July 1, 2025
概括
这项研究引入了一个深度学习 (DL) 模型,以优化体育教育 (PE) 时间表,提高学生的健康状况. 这种新的方法使PE个性化,以满足个人的需求,改善健康结果.
科学领域:
- 教育技术的教育技术
- 运动科学 运动科学 运动科学
- 人工智能的人工智能
背景情况:
- 体育教育 (PE) 对学生的健康和福祉至关重要.
- 优化运动计划是最大限度地提高健身的关键.
- 传统的日程安排方法往往忽略了个人的人口统计和活动差异.
研究的目的:
- 提出一种高效的深度学习 (DL) 方法,以优化体育教育 (PE) 时间表.
- 开发一个DL模型,将空间和时间模式集成为个性化的健身预测.
- 为各种学生群体增强PE课程的有效性.
主要方法:
- 开发了一个结合卷积神经网络 (CNN) 和长短期记忆 (LSTM) 层的深度学习 (DL) 模型.
- 该模型处理人口和活动数据以捕捉空间和时间特征.
- 一个融合层集成功能,一个定制的损失函数预测健身成绩.
主要成果:
- 拟议的DL模型在优化PE时间表方面显著优于基线方法.
- 取得的改进包括1.35%的平均平方误差 (MSE),1.18%的R平方 (R2) 和1.22%的平均绝对误差 (MAE).
- 实验评估证实了该模型的卓越预测准确性和效率.
结论:
- 深度学习为优化体育教育 (PE) 时间表提供了一种有效的策略.
- 个性化PE安排可以提高学生的健康状况和长期的健康益处.
- 这种模式支持教育机构创建定制和有效的PE计划.
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