# A multimodal sensor dataset for continuous stress detection of nurses in a hospital

arXiv: https://arxiv.org/abs/2108.07689 · HTML: https://mlsanity.com/paper/2108.07689/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jul 2021.

## Built on by (most cited first, 14 of 14)

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- [Ensemble Machine Learning Model Trained on a New Synthesized Dataset Generalizes Well for Stress Prediction Using Wearable Devices](https://mlsanity.com/paper/2209.15146/) (2022)
- [A Multimodal Intermediate Fusion Network with Manifold Learning for Stress Detection](https://mlsanity.com/paper/2403.08077/) (2024)
- [Multimodal Stress Detection Using Facial Landmarks and Biometric Signals](https://mlsanity.com/paper/2311.03606/) (2023)
- [ICYM2I: The illusion of multimodal informativeness under missingness](https://mlsanity.com/paper/2505.16953/) (2025)
- [Human Heterogeneity Invariant Stress Sensing](https://mlsanity.com/paper/2506.02256/) (2025)
- [Stress Monitoring in Healthcare: An Ensemble Machine Learning Framework Using Wearable Sensor Data](https://mlsanity.com/paper/2507.07589/) (2025)
- [A foundation model for electrodermal activity data](https://mlsanity.com/paper/2603.16878/) (2026)
- [Wearable data from subjects playing Super Mario, sitting university exams, or performing physical exercise help detect acute mood episodes via self-supervised learning](https://mlsanity.com/paper/2311.04215/) (2023)
- [AnnoSense: A Framework for Physiological Emotion Data Collection in Everyday Settings for AI](https://mlsanity.com/paper/2508.02680/) (2025)
- [Evaluation of Stress Detection as Time Series Events -- A Novel Window-Based F1-Metric](https://mlsanity.com/paper/2509.03240/) (2025)
- [Bespoke Co-processor for Energy-Efficient Health Monitoring on RISC-V-based Flexible Wearables](https://mlsanity.com/paper/2511.05985/) (2025)
- [From Pattern Recognizers to Personalized Companions: A Survey of Large Language Models in Mental Health](https://mlsanity.com/paper/2609.25186/) (2026)

## Abstract (arXiv)

Advances in wearable technologies provide the opportunity to monitor many physiological variables continuously. Stress detection has gained increased attention in recent years, mainly because early stress detection can help individuals better manage health to minimize the negative impacts of long-term stress exposure. This paper provides a unique stress detection dataset created in a natural working environment in a hospital. This dataset is a collection of biometric data of nurses during the COVID-19 outbreak. Studying stress in a work environment is complex due to many social, cultural, and psychological factors in dealing with stressful conditions. Therefore, we captured both the physiological data and associated context pertaining to the stress events. We monitored specifc physiological variables such as electrodermal activity, Heart Rate, and skin temperature of the nurse subjects. A periodic smartphone-administered survey also captured the contributing factors for the detected stress events. A database containing the signals, stress events, and survey responses is publicly available on Dryad.