UStress: Understanding college student subjective stress using wrist-based passive sensing

Begum Egilmez, Emirhan Poyraz, Wenting Zhou, Gokhan Memik, Peter A Dinda, Nabil Alshurafa

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

23 Scopus citations

Abstract

Stress plays a major role in physical and emotional well-being, and is associated with several illnesses including depression, diabetes, and other chronic diseases. College student stress as a construct is important to detect in order to equip students in a timely manner with stress coping strategies. However, the lack of a passive sensing measure accepted as a gold standard impedes real time detection and treatment of stress. Many researchers are studying passive sensing of stress using wrist-worn sensors; however, this effort focuses on understanding the essential features of wrist-worn sensors in detecting stress, and how to best induce stress in a lab setting. Applying machine learning methods increasingly is making it feasible to validly infer in real time through passive sensing of physical features psychological states, such as stress. Given strong participant adherence to wrist-worn sensors, this paper focuses on analyzing the effect of replacing other body sensing platforms (e.g. chest-based heart rate) with their wrist-worn equivalent on stress prediction accuracy. Nine participants were equipped with multiple body sensors and were asked to wear a commercially available Android smartwatch, a custom-designed smartwatch equipped with a Galvanic Skin Response (GSR) sensor, a chest-based heart rate sensor, and a finger-based commercial GSR sensor. Based on participant self-reports, singing experiment showed greatest stress levels across participants. This paper further analyzes features for prediction from all sensors compared to wrist-worn only sensors. Using statistical features on one-minute fixed-time sub-divisions and correlation-based feature subset selection and a Random Forest model, the system is capable of detecting stress with 88.8% F-measure.

Original languageEnglish (US)
Title of host publication2017 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages673-678
Number of pages6
ISBN (Electronic)9781509043385
DOIs
StatePublished - May 2 2017
Event2017 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2017 - Kona, Big Island, United States
Duration: Mar 13 2017Mar 17 2017

Publication series

Name2017 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2017

Other

Other2017 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2017
CountryUnited States
CityKona, Big Island
Period3/13/173/17/17

Keywords

  • Android
  • Galvanic skin response
  • Heart rate
  • Machine learning
  • Passive sensing
  • Smartwatch
  • Stress detection
  • Wrist sensors

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications

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