Stress recognition using wearable sensors and mobile phones

Akane Sano, Rosalind W. Picard

Research output: Chapter in Book/Report/Conference proceedingConference contribution

386 Scopus citations

Abstract

In this study, we aim to find physiological or behavioral markers for stress. We collected 5 days of data for 18 participants: a wrist sensor (accelerometer and skin conductance), mobile phone usage (call, short message service, location and screen on/off) and surveys (stress, mood, sleep, tiredness, general health, alcohol or caffeinated beverage intake and electronics usage). We applied correlation analysis to find statistically significant features associated with stress and used machine learning to classify whether the participants were stressed or not. In comparison to a baseline 87.5% accuracy using the surveys, our results showed over 75% accuracy in a binary classification using screen on, mobility, call or activity level information (some showed higher accuracy than the baseline). The correlation analysis showed that the higher-reported stress level was related to activity level, SMS and screen on/off patterns.

Original languageEnglish (US)
Title of host publicationProceedings - 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction, ACII 2013
Pages671-676
Number of pages6
DOIs
StatePublished - 2013
Event2013 5th Humaine Association Conference on Affective Computing and Intelligent Interaction, ACII 2013 - Geneva, Switzerland
Duration: Sep 2 2013Sep 5 2013

Publication series

NameProceedings - 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction, ACII 2013

Conference

Conference2013 5th Humaine Association Conference on Affective Computing and Intelligent Interaction, ACII 2013
Country/TerritorySwitzerland
CityGeneva
Period9/2/139/5/13

Keywords

  • Accelerometer
  • Classification
  • Machine learning
  • Mobile phone
  • Skin conductance
  • Smart phone
  • Stress
  • Wearable sensor

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Human-Computer Interaction

Fingerprint

Dive into the research topics of 'Stress recognition using wearable sensors and mobile phones'. Together they form a unique fingerprint.

Cite this