在急性压力和运动条件下的可穿戴生理信号
Andrea Hongn1,2, Facundo Bosch3, Lara Eleonora Prado3
1Instituto de Ingeniería Biomédica (IIBM), Facultad de Ingeniería, Buenos Aires, Argentina. ahongn.ext@fi.uba.ar.
Scientific data
|March 29, 2025
概括
介绍了一套新数据集,包括压力和运动期间可穿戴传感器发出的生理信号. 机器学习模型准确地对这些状态进行分类,使得生理监测领域的新研究成为可能.
科学领域:
- 生理监测和生物信号分析.
- 可穿戴式传感器技术和数据采集.
- 机器学习在健康和运动科学中的应用.
背景情况:
- 准确的生理状态监测对于健康和绩效研究至关重要.
- 现有的数据集可能在各种活动中缺乏全面的生理信号.
- 非侵入性,可穿戴式传感器为持续数据收集提供了一个有希望的途径.
研究的目的:
- 提出一种新的,公开可用的生理信号数据集.
- 在急性应激诱导和不同的运动类型 (有氧/无氧) 中捕获数据.
- 用机器学习分类来验证数据集的实用性.
主要方法:
- 使用Empatica E4可穿戴设备收集的非侵入性生理数据 (电皮活动,皮肤温度,加速度计,血液体积脉冲).
- 开发了一个针对急性压力诱导 (数学/情感任务) 和运动 (静止自行车) 的结构化协议.
- 记录了36个健康个体的数据,包括压力,有氧和无氧条件,有些重叠.
主要成果:
- XGBoost机器学习算法在分类任务中实现了高精度.
- 在区分压力与休息时,准确度为93%.
- 在区分有氧和无氧运动时,准确率为91%.
- 在四个标签分类 (压力,休息,有氧,无氧) 中,准确度为84%.
结论:
- 本文所介绍的数据集是研究生理监测和机器学习的宝贵资源.
- 这些数据有效地支持不同生理状态的分类.
- 该数据集是公开可访问的,以促进进一步的科学调查.
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