# Automatic Stress Detection in Working Environments from Smartphones' Accelerometer Data: A First Step

arXiv: https://arxiv.org/abs/1510.04221 · HTML: https://mlsanity.com/paper/1510.04221/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Oct 2015.

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

- [Posttraumatic Stress Disorder Hyperarousal Event Detection Using Smartwatch Physiological and Activity Data](https://mlsanity.com/paper/2109.14743/) (2021)
- [Stress Classification and Personalization: Getting the most out of the least](https://mlsanity.com/paper/2107.05666/) (2021)
- [Wearable and Continuous Prediction of Passage of Time Perception for Monitoring Mental Health](https://mlsanity.com/paper/2105.02808/) (2021)
- [Human Stress Assessment: A Comprehensive Review of Methods Using Wearable Sensors and Non-wearable Techniques](https://mlsanity.com/paper/2202.03033/) (2022)
- [Multimodal Stress Detection Using Facial Landmarks and Biometric Signals](https://mlsanity.com/paper/2311.03606/) (2023)
- [A Generic Review of Integrating Artificial Intelligence in Cognitive Behavioral Therapy](https://mlsanity.com/paper/2407.19422/) (2024)
- [Extending Stress Detection Reproducibility to Consumer Wearable Sensors](https://mlsanity.com/paper/2505.05694/) (2025)
- [Predicting mental health using social media: A roadmap for future development](https://mlsanity.com/paper/2301.10453/) (2023)
- [Conformal Prediction in Multi-User Settings: An Evaluation](https://mlsanity.com/paper/2312.05195/) (2023)
- [IoT-Based Preventive Mental Health Using Knowledge Graphs and Standards for Better Well-Being](https://mlsanity.com/paper/2406.13791/) (2024)
- [Predicting Human Depression with Hybrid Data Acquisition utilizing Physical Activity Sensing and Social Media Feeds](https://mlsanity.com/paper/2505.22779/) (2025)
- [AdaptStress: Online Adaptive Learning for Interpretable and Personalized Stress Prediction Using Multivariate and Sparse Physiological Signals](https://mlsanity.com/paper/2602.18521/) (2026)

## Abstract (arXiv)

Increase in workload across many organisations and consequent increase in occupational stress is negatively affecting the health of the workforce. Measuring stress and other human psychological dynamics is difficult due to subjective nature of self- reporting and variability between and within individuals. With the advent of smartphones it is now possible to monitor diverse aspects of human behaviour, including objectively measured behaviour related to psychological state and consequently stress. We have used data from the smartphone's built-in accelerometer to detect behaviour that correlates with subjects stress levels. Accelerometer sensor was chosen because it raises fewer privacy concerns (in comparison to location, video or audio recording, for example) and because its low power consumption makes it suitable to be embedded in smaller wearable devices, such as fitness trackers. 30 subjects from two different organizations were provided with smartphones. The study lasted for 8 weeks and was conducted in real working environments, with no constraints whatsoever placed upon smartphone usage. The subjects reported their perceived stress levels three times during their working hours. Using combination of statistical models to classify self reported stress levels, we achieved a maximum overall accuracy of 71% for user-specific models and an accuracy of 60% for the use of similar-users models, relying solely on data from a single accelerometer.