基于视觉的多式联动能源消耗估计成人有氧运动的能量消耗
Lei Jin1, Shengxuming Zhang2, Mingyao Shi2
1Department of Sports Science, Zhejiang University, Hangzhou, China.
Frontiers in physiology
|October 24, 2025
概括
本研究介绍了E3SFormer,这是一种基于变压器的新方法,用于无接触式能源消耗 (EE) 估计. 该模型使用骨架数据和多模式输入实现准确的结果,优于现有方法和智能手表.
科学领域:
- 运动科学 运动科学 运动科学
- 计算机视觉 计算机视觉
- 生物医学工程 生物医学工程
背景情况:
- 在体育科学中,精确且方便地估计能源消耗 (EE) 是至关重要的.
- 传统的方法往往需要专门的设备或是不方便的连续监控.
- 计算机视觉 (CV) 和深度学习的进步,特别是变压器,为非接触式生理分析提供了新的可能性.
研究的目的:
- 提出和评估一种基于变压器的新方法,用于无接触和基于视觉的能源支出估计.
- 为了利用骨架数据进行同时动作识别和EE回归.
- 探索多模式输入的潜力,以提高EE估计的准确性.
主要方法:
- 收集了16526段36名参与者进行6项有氧运动的视频片段,使用COSMED K5.5的EE数据.
- 开发了具有双变压器分支的能源支出估计骨架变压器 (E3SFormer).
- 将E3SFormer的性能与基于骨架的动作识别模型,传统的心率公式和使用平均相对误差 (MRE) 的智能手表进行了比较.
主要成果:
- E3SFormer仅使用骨架输入实现了28.81%的MRE,超过了所有比较模型.
- 通过多模输入 (心率和物理属性),E3SFormer达到15.32%的MRE,明显优于其他方法.
- 相比之下,一个商业智能手表显示了18.10%的MRE.
结论:
- E3SFormer模型在使用骨架和多模式数据进行非接触式EE估计方面表现出高效率.
- 这项研究是第一个利用变压器模型进行EE估计的研究,为空气运动中的非接触性生理分析铺平了道路.
- 这些发现鼓励进一步研究基于视觉和多模式的非侵入性生理监测方法.
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