通过多模型卡尔曼过和基于差异的融合,从心率估计核心体温
Yuanzhe Zhao1, Jeroen Hm Bergmann1,2
1Department of Engineering Science, University of Oxford, Oxford, United Kingdom.
Physiological measurement
|October 2, 2025
概括
这项研究开发了一个新的框架,仅使用心率 (HR) 数据来非侵入性地估计核心体温 (CBT). 这种新的方法为可穿戴设备提供了准确的实时热应变监测.
科学领域:
- 生理监测是指对身体进行生理监测.
- 生物医学工程 生物医学工程
- 可穿戴技术是可穿戴的技术.
背景情况:
- 准确的核心体温 (CBT) 估计对于预防与热有关的疾病至关重要.
- 目前用于CBT监测的方法通常是侵入性的或不切实际的,在体育活动期间连续使用.
- 对于可穿戴应用,需要进行非侵入性,实时的CBT估计.
研究的目的:
- 开发和评估一个实时CBT估计的框架,仅使用心率 (HR) 数据.
- 创建一个轻量级的解决方案,可部署在可穿戴设备上,用于热应力监测.
- 为了比较基于HR的CBT估计的监督和无监督建模方法的性能.
主要方法:
- 提出了一个多模型卡尔曼过 (KF) 框架,采用基于差异的融合.
- 开发了使用活动标签 (休息,运动,恢复) 的监督生理状态特定KF (PSSK).
- 开发了一种基于集群的无监督试验KF (TCBK),以捕捉没有状态注释的生理变异性.
- 在两个独立的数据集上对两个模型进行了评估,并与基线方法对比.
主要成果:
- 在数据集内评估中,TCBK取得了最高的准确性 (RMSE:0.38°C - 0.41°C).
- 普斯克表现出优越的跨数据集概括稳定性 (RMSE:0.88°C).
- 无论是PSSK还是TCBK模型都表现优于已有的Buller和Falcone模型.
结论:
- 轻量级,仅用于HR的模型可以通过状态或上下文意识的建模实现准确的CBT估计.
- 拟议的框架为职业和体育环境中持续的热应变监测提供了一个实际的解决方案.
- 这种方法平衡了可穿戴技术的性能和现实应用性.
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