基于生理信号的压力分析概述:对开放数据集和当前趋势的系统审查
Chariklia Chatzaki1, Manolis Tsiknakis1,2
1Biomedical Informatics and eHealth Laboratory, Department of Electrical and Computer Engineering, Hellenic Mediterranean University, Estavromenos, 71004 Heraklion, Greece.
Sensors (Basel, Switzerland)
|December 11, 2025
概括
本综述分析了开放式访问压力数据集和计算方法. 它强调了可重现性的挑战,并提出了使用多式生理和行为数据可靠压力建模的框架.
科学领域:
- 生理学和计算科学 生理学和计算科学
- 情感计算是一种情感计算.
- 在医疗保健中的数据科学.
背景情况:
- 压力研究依赖于各种数据集和分析方法.
- 开放式数据集对于推进压力分析至关重要.
- 可复制性和符合FAIR标准是当前压力研究中的关键挑战.
研究的目的:
- 审查压力研究的开放式情感数据集.
- 分析压力分析中的方法趋势.
- 为了确定可复制性和FAIR合规性的差距.
主要方法:
- 从2014年到2024年对文献进行系统审查.
- 识别和分析32个开放访问情感数据集.
- 审查最近的信号分析方法 (2023-2025年).
主要成果:
- 确定了32个具有多模式生理 (EEG,ECG,EDA,Resp) 和行为数据的开放访问数据集.
- 最近的信号分析趋势包括多式联接,深度学习,自我监督学习和可解释的人工智能.
- 关键限制包括异质设计,传感器可变性,有限的人口统计和数据标签问题.
结论:
- 一个有效的压力研究框架需要综合的人口统计,可靠的压力因素和高质量的生理数据.
- 实验的标准化和可解释的建模对于可重现和可概括的压力模型至关重要.
- 解决数据异质性和偏差对于验证计算压力模型至关重要.
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