Introduction. The stress response has both subjective, psychological and objectively measurable, biological components. Both of them can be expressed differently from person to person, complicating the development of a generic stress measurement model. This is further compounded by the lack of large, labeled datasets that can be utilized to build machine learning models for accurately detecting periods and levels of stress. The aim of this review is to provide an overview of the current state of stress detection and monitoring using wearable devices, and where applicable, machine learning techniques utilized. Methods. This study reviewed published works contributing and/or using datasets designed for detecting stress and their associated machine learning methods, with a systematic review and meta-analysis of those that utilized wearable sensor data as stress biomarkers. The electronic databases of Google Scholar, Crossref, DOAJ and PubMed were searched for relevant articles and a total of 24 articles were identified and included in the final analysis. The reviewed works were synthesized into three categories of publicly available stress datasets, machine learning, and future research directions. Results. A wide variety of study-specific test and measurement protocols were noted in the literature. A number of public datasets were identified that are labeled for stress detection. In addition, we discuss that previous works show shortcomings in areas such as their labeling protocols, lack of statistical power, validity of stress biomarkers, and generalization ability. Conclusion. Generalization of existing machine learning models still require further study, and research in this area will continue to provide improvements as newer and more substantial datasets become available for study.

翻译：引言。压力反应既包含主观心理层面，也包含可客观测量的生物学成分。这两方面在不同个体间存在差异性表达，使得通用压力测量模型的开发变得复杂。这一困境因缺乏可构建机器学习模型以准确检测压力时期和强度的大规模标注数据集而进一步加剧。本综述旨在概述当前利用可穿戴设备进行压力检测与监测的研究现状，并重点关注其中采用的机器学习技术。方法。本研究系统检索了Google Scholar、Crossref、DOAJ和PubMed等电子数据库中相关文献，针对以可穿戴传感器数据为压力生物标志物的研究进行了系统性综述和荟萃分析，最终纳入24篇论文进行综合分析。综述内容被归纳为三类：公开可用的压力数据集、机器学习方法及未来研究方向。结果。文献中观察到多种针对特定研究的测试与测量方案，同时识别出数个已标注压力检测标签的公开数据集。此外，我们指出既往研究在标注协议、统计效力不足、压力生物标志物有效性及泛化能力等方面存在局限。结论。现有机器学习模型的泛化能力仍需深入研究，随着更多大规模数据集的可用性提升，该领域研究将持续取得进展。