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基于可穿戴传感器数据的机器学习用于预测压力事件:系统性审查

António Oseas Pataca¹, Eftim Zdravevski², Paulo Jorge Coelho³

¹Instituto de Telecomunicações, Escola Superior de Tecnologia e Gestão de Águeda, Universidade de Aveiro, Águeda, Portugal.

Computers in biology and medicine

|October 8, 2025

概括

可穿戴传感器和机器学习 (ML) 准确地使用生理数据预测压力事件. 这种整合可以通过实时压力监测和个性化干预来实现主动的医疗保健.

科学领域:

生物医学工程生物医学工程
计算机科学计算机科学
心理学心理学心理学

背景情况:

压力监测对心理和身体健康至关重要.
传统的压力评估方法往往是主观的,耗时的.
可穿戴技术提供持续的,客观的生理数据收集.

研究的目的:

系统地审查可穿戴传感器数据和机器学习 (ML) 的整合,以预测压力.
识别流行传感器,关键生理特征和ML模型有效性.
探索现实世界的压力监测和预测的潜力.

主要方法:

采用PRISMA方法的系统文献审查 (2010年1月 - 2025年6月).
数据来源:IEEE Xplore,Elsevier,Springer,MDPI,PubMed Central,ACM. 这些数据来源均来自美国.
包括标准:可穿戴设备,ML压力预测,生理数据 (HRV,皮肤导电,睡眠).

主要成果:

使用各种传感器 (EDA,PPG,加速度计) 和特征 (HRV,皮肤导电量,呼吸模式).
机器学习模型 (RF,SVM,DNN) 显示出高预测准确度 (高达99%).
挑战包括小样本大小,数据变化以及缺乏标准化协议.

结论:

可穿戴传感器和机器学习为主动医疗保健提供可扩展的实时压力监测.
进步需要通过跨学科合作解决局限性,并提高技术可访问性.
这个领域对个性化健康,心理健康支持和医疗保健效率有着变革性的影响.

关键词:

机器学习机器学习传感器数据传感器数据传感器传感器传感器压力预测压力预测可穿戴设备可以穿戴.

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