Machine Learning for Stress Monitoring from Wearable Devices: A Systematic Literature Review

Introduction. Wearable sensors have shown promise as a non-intrusive method for collecting biomarkers that may correlate with levels of elevated stress. 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 datasets publicly available are usually collected using different devices and experimental settings, and are labeled using differing scoring methods. 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. We also shed light on the challenges and opportunities that machine learning-enabled stress monitoring and detection face. 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. For the machine learning studies reviewed, we provide an analysis of their approach Preprint submitted to Artificial Intelligence in Medicine October 3, 2022 to the different steps of a machine learning development pipeline, further synthesizing the finding and trends in the literature. We also provide a review of several major wearable devices that are able to measure four biomarkers known to be robust indicators of elevated levels of stress. 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. We focused mainly on datasets using the Empatica E4 device, a well-studied, easy to access, United States Food and Drug Administration (FDA) approved wrist-worn wearable that provides sensor biomarkers most notable to correlate with elevated levels of stress. Most datasets contain less than twenty-four hours of data, and the varied experimental conditions and labeling methodologies potentially limit their ability to generalize for unseen data. 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. Health tracking and monitoring using wearable devices is growing in popularity, and continued interest with a growing number of studies in this area of health care will likely improve the accuracy of detecting specific health conditions including elevated levels of stress. There is a need for a definitive device-use and setup protocol to enable consistent results across tests with medical-grade wearable devices. 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.