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

arXiv: https://arxiv.org/abs/2209.15137 · HTML: https://mlsanity.com/paper/2209.15137/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 89 references that are in the map.
- First posted on arXiv 29 Sep 2022.

## Builds on (most cited first, 6 of 6)

- [Hardware Implementation of Deep Network Accelerators Towards Healthcare and Biomedical Applications](https://mlsanity.com/paper/2007.05657/) (2020)
- [K-EmoCon, a multimodal sensor dataset for continuous emotion recognition in naturalistic conversations](https://mlsanity.com/paper/2005.04120/) (2020)
- [The Effect of Person-Specific Biometrics in Improving Generic Stress Predictive Models](https://mlsanity.com/paper/1910.01770/) (2019)
- [IB-GAN: A Unified Approach for Multivariate Time Series Classification under Class Imbalance](https://mlsanity.com/paper/2110.07460/) (2021)
- [Monitoring My Dehydration: A Non-Invasive Dehydration Alert System Using Electrodermal Activity](https://mlsanity.com/paper/2009.13626/) (2020)
- [A Systematic Literature Review on Wearable Health Data Publishing under Differential Privacy](https://mlsanity.com/paper/2109.07334/) (2021)

## Built on by (most cited first, 12 of 12)

- [Stressor Type Matters! -- Exploring Factors Influencing Cross-Dataset Generalizability of Physiological Stress Detection](https://mlsanity.com/paper/2405.09563/) (2024)
- [Extending Stress Detection Reproducibility to Consumer Wearable Sensors](https://mlsanity.com/paper/2505.05694/) (2025)
- [Investigating the Generalizability of Physiological Characteristics of Anxiety](https://mlsanity.com/paper/2402.15513/) (2024)
- [CurvFed: Curvature-Aligned Federated Learning for Fairness without Demographics](https://mlsanity.com/paper/2404.19725/) (2024)
- [A State-of-the-Art Review of Computational Models for Analyzing Longitudinal Wearable Sensor Data in Healthcare](https://mlsanity.com/paper/2407.21665/) (2024)
- [Human Heterogeneity Invariant Stress Sensing](https://mlsanity.com/paper/2506.02256/) (2025)
- [Protecting Student Mental Health with a Context-Aware Machine Learning Framework for Stress Monitoring](https://mlsanity.com/paper/2508.01105/) (2025)
- [Ethical Fairness in Ubiquitous Health Sensing without Known Attributes](https://mlsanity.com/paper/2603.13373/) (2026)
- [Empowering Precision Medicine: AI-Driven Schizophrenia Diagnosis via EEG Signals: A Comprehensive Review from 2002-2023](https://mlsanity.com/paper/2309.12202/) (2023)
- [Socially Interactive Agents for Robotic Neurorehabilitation Training: Conceptualization and Proof-of-concept Study](https://mlsanity.com/paper/2406.12035/) (2024)
- [The Effect of Acute Stress on the Interpretability and Generalization of Schizophrenia Predictive Machine Learning Models](https://mlsanity.com/paper/2410.19739/) (2024)
- [Stress Estimation in Elderly Oncology Patients Using Visual Wearable Representations and Multi-Instance Learning](https://mlsanity.com/paper/2604.06990/) (2026)

## Abstract (arXiv)

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.