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.Comment: 50 pages, 8 figure

Azghadi, Mostafa Rahimi

Sarnyai, Zoltan

Trinh, Kelly

Vos, Gideon

English

arXiv

Introduction: Wearable sensors have shown promise as a non-intrusive method for collecting biomarkers that may correlate with levels of elevated stress. Stressors cause a variety of biological responses, and these physiological reactions can be measured using biomarkers including Heart Rate Variability (HRV), Electrodermal Activity (EDA) and Heart Rate (HR) that represent the stress response from the Hypothalamic-Pituitary-Adrenal (HPA) axis, the Autonomic Nervous System (ANS), and the immune system. While Cortisol response magnitude remains the gold standard indicator for stress assessment [1], recent advances in wearable technologies have resulted in the availability of a number of consumer devices capable of recording HRV, EDA and HR sensor biomarkers, amongst other signals. At the same time, researchers have been applying machine learning techniques to the recorded biomarkers in order to build models that may be able to predict elevated levels of stress.



Objective: The aim of this review is to provide an overview of machine learning techniques utilized in prior research with a specific focus on model generalization when using these public datasets as training data. 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 public datasets designed for detecting stress and their associated machine learning methods. The electronic databases of Google Scholar, Crossref, DOAJ and PubMed were searched for relevant articles and a total of 33 articles were identified and included in the final analysis. The reviewed works were synthesized into three categories of publicly available stress datasets, machine learning techniques applied using those, and future research directions. For the machine learning studies reviewed, we provide an analysis of their approach to results validation and model generalization. The quality assessment of the included studies was conducted in accordance with the IJMEDI checklist [2].



Results: A number of public datasets were identified that are labeled for stress detection. These datasets were most commonly produced from sensor biomarker data recorded using the Empatica E4 device, a well-studied, medical-grade wrist-worn wearable that provides sensor biomarkers most notable to correlate with elevated levels of stress. Most of the reviewed 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 model generalization ability.



Conclusion: Health tracking and monitoring using wearable devices is growing in popularity, while the generalization of existing machine learning models still requires further study, and research in this area will continue to provide improvements as newer and more substantial datasets become available

Rahimi Azghadi, Mostafa

ResearchOnline at James Cook University

Generalizable machine learning for stress monitoring from wearable devices: A systematic literature review

arXiv.org e-Print Archive

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

https://researchonline.jcu.edu.au/78315/1/78315.pdf

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

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ResearchOnline at James Cook University

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