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 language | English |
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Title of host publication | Smart Technologies for Sustainable and Resilient Ecosystems |
Subtitle of host publication | 3rd EAI International Conference, Edge-IoT 2022, and 4th EAI International Conference, SmartGov 2022, Virtual Events, November 16-18, 2022, Proceedings |
Editors | Sérgio Ivan Lopes, Paula Fraga-Lamas, Tiago M. Fernándes-Camáres, Babu R. Dawadi, Subarna Shakya, Danda B. Rawat |
Place of Publication | Cham |
Publisher | Springer |
Pages | 130-151 |
Number of pages | 22 |
ISBN (Electronic) | 978-3-031-35982-8 |
ISBN (Print) | 978-3-031-35981-1 |
DOIs | |
Publication status | Published - 2023 |
Externally published | Yes |
Event | 3rd International Conference on Intelligent Edge Processing in the IoT Era, Edge-IoT 2022 - Virtual, Online Duration: 16 Nov 2022 → 18 Nov 2022 Conference number: 3 |
Publication series
Name | Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST |
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Volume | 510 LNICST |
ISSN (Print) | 1867-8211 |
ISSN (Electronic) | 1867-822X |
Conference
Conference | 3rd International Conference on Intelligent Edge Processing in the IoT Era, Edge-IoT 2022 |
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Abbreviated title | Edge-IoT 2022 |
City | Virtual, Online |
Period | 16/11/22 → 18/11/22 |
Keywords
- Classification
- Daily life
- Digital biomarkers
- Machine Learning (ML)
- Sensors
- Stress
- Wearable computing
- n/a OA procedure