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

TL;DR

This study explores using smartphone accelerometer data to objectively detect occupational stress levels in real work environments, achieving up to 71% accuracy with personalized models.

Contribution

It demonstrates the feasibility of stress detection using only accelerometer data in real-world settings, with a focus on privacy and low power consumption.

Findings

Achieved 71% accuracy with user-specific models.

Achieved 60% accuracy with similar-users models.

Validated accelerometer-based stress detection in real workplaces.

Abstract

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.