皮肤电活动（EDA）用于压力检测：综合综述与基准测试
Electrodermal Activity (EDA) for Stress Detection: A Comprehensive Survey and Benchmark
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中文总结 AI 辅助
本文对基于皮肤电活动的压力检测进行综合综述与基准测试，汇编26个数据集和22项研究，验证cvxEDA，发现特征型机器学习优于端到端深度学习，并强调生态效度对跨域迁移的重要性。
中文摘要 AI 辅助
皮肤电活动（EDA）因其对交感神经唤醒的敏感性以及计算效率，被广泛用于普适压力监测。然而，相关研究仍较为分散：研究集中于可用数据集中的一小部分，预处理和评估协议不一致，且广泛采用的方法学惯例尚未得到验证。现有综述总结了这些实践，但未在统一协议下对其进行评估。为弥补这些不足，本文对基于EDA的压力检测进行了综合综述与基准测试。我们回顾了从预处理到建模的技术流程，并汇编了26个公开数据集和22项具有代表性的单模态研究，以指导未来的数据集选择和方法学设计。在涵盖五个不同数据集的受试者独立协议下，我们对分解、滤波和通道输入选择以及广泛的机器学习（ML）和深度学习（DL）模型在域内和跨域迁移设置中进行了基准测试。我们的结果首次对默认分解方法cvxEDA进行了多数据集验证，表明基于特征的ML优于端到端（E2E）DL，并指出生态效度是跨域迁移的关键因素。最后，我们提出了实现稳健、数据高效和可信部署的方向。
英文摘要
Electrodermal activity (EDA) is widely used for pervasive stress monitoring because of its sensitivity to sympathetic arousal and its computational efficiency. However, research remains fragmented: studies concentrate on a small subset of available datasets, preprocessing and evaluation protocols are inconsistent, and widely adopted methodological conventions remain unvalidated. Existing reviews summarize these practices without evaluating them under a common protocol. To bridge these gaps, this paper presents a comprehensive survey and benchmark for EDA-based stress detection. We review the technical pipeline from preprocessing to modeling and compile 26 public datasets and 22 representative unimodal studies to inform future dataset selection and methodological design. Under a subject-independent protocol spanning five diverse datasets, we benchmark decomposition, filtering, and channel-input choices alongside a broad range of machine learning (ML) and deep learning (DL) models in both in-domain and cross-domain transfer settings. Our results provide the first multi-dataset validation of cvxEDA, the default decomposition method, show that feature-based ML outperforms end-to-end (E2E) DL, and identify ecological validity as a key factor in cross-domain transfer. We conclude with directions toward robust, data-efficient, and trustworthy deployment.
发表机构
- University of Notre Dame（圣母大学）
机构由 AI 辅助整理，请以论文原文为准。
