超越EDA:对多模态交感神经系统激发分类用于压力检测的系统性审查
Santiago Sosa1, Adam K Fontecchio1, Evangelia G Chrysikou2
1Department of Electrical and Computer Engineering, Drexel University, Philadelphia, PA 19104, USA.
Sensors (Basel, Switzerland)
|March 14, 2026
概括
本综述探讨了多式可穿戴系统,通过将皮电活动 (EDA) 与其他生物信号相结合来评估交感神经系统 (SNS) 兴奋. 它强调了传感器融合和机器学习的进步,以增强兴奋推断.
科学领域:
- 生理监测是指对身体进行生理监测.
- 可穿戴技术是可穿戴的技术.
- 人与计算机的互动.
背景情况:
- 皮电活动 (EDA) 是交感神经系统 (SNS) 兴奋的关键指标.
- 单传感器分析是有限的;多式联运系统将EDA与其他生物信号 (HRV,PPG,SKT,SPO2) 整合起来正在出现.
- 现有的审查主要集中在EDA上,忽视了多式联运方法.
研究的目的:
- 为SNS兴奋评估提供多式联网可穿戴系统的全面分析.
- 绘制当前传感器集成和信号处理技术的地图.
- 确定在上下文感知SNS兴奋感知方面的进展和未来研究方向.
主要方法:
- 对58项符合纳入标准的研究进行系统性文献审查.
- 分析多模式传感器集成,从单个信号到复杂的传感器融合.
- 检查信号处理和机器学习技术用于唤醒推断.
主要成果:
- 多模式系统提供了一个比单独的EDA更全面的评估生理兴奋.
- 传感器模型,生理模型和情境感知传感器的进步是显而易见的.
- 在信号处理和机器学习方面取得的重大进展增强了SNS兴奋推断.
结论:
- 多模式感应在生理兴奋研究中代表了显著的方法转变.
- 多个生物信号的集成为SNS活动提供了更丰富的见解.
- 本综述是开发下一代兴奋感应技术的基础资源.
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