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

• Published in: IEEE journal of biomedical and health informatics Vol. 20; no. 4; pp. 1053 - 1060
• Main Authors: Garcia-Ceja, Enrique, Osmani, Venet, Mayora, Oscar
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.07.2016; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accelerometer; Accelerometers; Accelerometry - instrumentation; Accuracy; Adaptation models; Adult; ambient intelligence; automatic stress detection; Data models; Feature extraction; Female; Health; health monitoring; Human Activities - classification; Human behavior; Humans; Male; Monitoring, Physiologic - instrumentation; Monitoring, Physiologic - methods; Occupational Stress - diagnosis; Organizations; Signal Processing, Computer-Assisted - instrumentation; Smart phones; Smartphone; Smartphones; Stress; Stresses; well-being smartphones; Working conditions
• ISSN: 2168-2194; 2168-2208
• DOI: 10.1109/JBHI.2015.2446195

Increase in workload across many organizations and consequent increase in occupational stress are negatively affecting the health of the workforce. Measuring stress and other human psychological dynamics is difficult due to subjective nature of selfreporting and variability between and within individuals. With the advent of smartphones, it is now possible to monitor diverse aspects of human behavior, including objectively measured behavior related to psychological state and consequently stress. We have used data from the smartphone's built-in accelerometer to detect behavior that correlates with subjects stress levels. Accelerometer sensor was chosen because it raises fewer privacy concerns (e.g., in comparison to location, video, or audio recording), and because its low-power consumption makes it suitable to be embedded in smaller wearable devices, such as fitness trackers. About 30 subjects from two different organizations were provided with smartphones. The study lasted for eight 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 selfreported 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.