  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.
