Abstract
In this work, our objective is to design, develop, and evaluate the effectiveness of a stress detection model for older adults using a system of wrist-worn sensors. Our system uses four signals, EDA, BVP, IBI, and ST from EDA, PPG, and ST sensors, embedded in a smart wristband, to classify between stressed and not-stressed state. The stress reference is obtained from salivary cortisol measurement, which is a well established clinical biomarker for measuring physiological stress. This work is the result of year-long data collection and analysis of 40 older adults (28 females and 12 males) and age 73.625 ± 5.39. EDA, BVP, IBI, and ST signals were collected during TSST (Trier Social Stress Test), which is a well known experimental protocol to reliably induce stress in humans in a social setting. 47 features were extracted from EDA, BVP, IBI, and ST signals, out of which 27 features were selected using a supervised feature selection method. Results and analysis show that combining the features from all the four signal streams increases the model's ability to accurately distinguish between the stressed and not-stressed states. The proposed model achieved a macro-average F1-score of 0.92 and an accuracy of 94% in distinguishing between the two states when features from all the four signals were used. Further, we prototype the proposed stress detection model in a consumer end device with voice capabilities, so that users can receive feedback on their vitals and stress levels by querying on voice-enabled consumer devices such as smartphones and smart speakers.
Original language | English |
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Article number | 9349773 |
Pages (from-to) | 30-39 |
Number of pages | 10 |
Journal | IEEE Transactions on Consumer Electronics |
Volume | 67 |
Issue number | 1 |
DOIs | |
State | Published - Feb 2021 |
Bibliographical note
Publisher Copyright:© 2020 IEEE.
Funding
Manuscript received July 7, 2020; revised November 29, 2020 and January 24, 2021; accepted February 3, 2021. Date of publication February 8, 2021; date of current version February 26, 2021. This work was supported by the Kentucky Science and Engineering Foundation under Grant KSEF-3528-RDE-019. (Corresponding author: Himanshu Thapliyal.) The authors are with the Department of Electrical and Computer Engineering, University of Kentucky, Lexington, KY 40506 USA (e-mail: [email protected]). Digital Object Identifier 10.1109/TCE.2021.3057806
Funders | Funder number |
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Kentucky Science and Engineering Foundation | KSEF-3528-RDE-019 |
Keywords
- Electrodermal activity (EDA)
- blood volume pulse (BVP)
- machine learning
- photoplethysmogram (PPG)
- salivary cortisol
- skin temperature (ST)
ASJC Scopus subject areas
- Media Technology
- Electrical and Electronic Engineering