一个连续的现实数据集,包括基于可穿戴的心率变化以及睡眠日记
Aitolkyn Baigutanova1,2, Sungkyu Park3,4, Marios Constantinides5,6
1Korea Advanced Institute of Science and Technology (KAIST), School of Computing, Daejeon, 34141, Republic of Korea.
Scientific data
|August 23, 2025
概括
这项研究引入了智能手表中的真实心率变化 (HRV) 的新数据集. 这些数据可以帮助开发使用可穿戴技术和睡眠模式的预测健康分析.
科学领域:
- 生理学和健康信息学
- 可穿戴技术
- 生物医学数据科学
背景情况:
- 心率变化 (HRV) 监测提供了有关健康状况的见解.
- 现有的HRV数据集缺乏现实世界的复杂性,在受控的临床环境下收集.
- 需要强大的自然生理数据来进行健康分析.
研究的目的:
- 使用智能手表收集和验证连续的真实生理和运动数据.
- 为野生动物的HRV记录建立一个基准数据集.
- 支持可穿戴数据和睡眠模式的预测健康分析的发展.
主要方法:
- 通过智能手表收集了49名健康成年人的持续生理和运动信号.
- 每隔100毫秒的采样记录,用于从5分钟段进行短期HRV计算.
- 通过信号频率分析,传感器-HRV相关性和基于文献的特征分布检查验证的数据.
主要成果:
- 在现实环境中成功收集了连续智能手表记录的新数据集.
- 验证了收集的生理和运动数据的质量和可靠性.
- 预期HRV和与睡眠相关的特征分布与现有文献一致.
结论:
- 开发的数据集为对野生动物的HRV进行比较提供了有价值的资源.
- 在未来的研究中分析现实世界的生理数据以获得健康见解.
- 支持使用可穿戴技术和睡眠数据进行健康监测的预测分析.
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