Extracting Digital Biomarkers for Unobtrusive Stress State Screening from Multimodal Wearable Data

Berrenur Saylam*, Özlem Durmaz İncel

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

With the development of wearable technologies, a new kind of healthcare data has become valuable as medical information. These data provide meaningful information regarding an individual’s physiological and psychological states, such as activity level, mood, stress, and cognitive health. These biomarkers are named digital since they are collected from digital devices integrated with various sensors. In this study, we explore digital biomarkers related to stress modality by examining data collected from mobile phones and smartwatches. We utilize machine learning techniques on the Tesserae dataset, precisely Random Forest, to extract stress biomarkers. Using feature selection techniques, we utilize weather, activity, heart rate (HR), stress, sleep, and location (work-home) measurements from wearables to determine the most important stress-related biomarkers. We believe we contribute to interpreting stress biomarkers with a high range of features from different devices. In addition, we classify the 5 different stress levels with the most important features, and our results show that we can achieve 85 % overall class accuracy by adjusting class imbalance and adding extra features related to personality characteristics. We perform similar and even better results in recognizing stress states with digital biomarkers in a daily-life scenario targeting a higher number of classes compared to the related studies.

Original languageEnglish
Title of host publicationSmart Technologies for Sustainable and Resilient Ecosystems
Subtitle of host publication3rd EAI International Conference, Edge-IoT 2022, and 4th EAI International Conference, SmartGov 2022, Virtual Events, November 16-18, 2022, Proceedings
EditorsSérgio Ivan Lopes, Paula Fraga-Lamas, Tiago M. Fernándes-Camáres, Babu R. Dawadi, Subarna Shakya, Danda B. Rawat
Place of PublicationCham
PublisherSpringer
Pages130-151
Number of pages22
ISBN (Electronic)978-3-031-35982-8
ISBN (Print)978-3-031-35981-1
DOIs
Publication statusPublished - 2023
Externally publishedYes
Event3rd International Conference on Intelligent Edge Processing in the IoT Era, Edge-IoT 2022 - Virtual, Online
Duration: 16 Nov 202218 Nov 2022
Conference number: 3

Publication series

NameLecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
Volume510 LNICST
ISSN (Print)1867-8211
ISSN (Electronic)1867-822X

Conference

Conference3rd International Conference on Intelligent Edge Processing in the IoT Era, Edge-IoT 2022
Abbreviated titleEdge-IoT 2022
CityVirtual, Online
Period16/11/2218/11/22

Keywords

  • Classification
  • Daily life
  • Digital biomarkers
  • Machine Learning (ML)
  • Sensors
  • Stress
  • Wearable computing
  • n/a OA procedure

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