Smart Wristband-Based Stress Detection Framework for Older Adults with Cortisol as Stress Biomarker

Rajdeep Kumar Nath, Himanshu Thapliyal

Research output: Contribution to journalArticlepeer-review

26 Scopus citations


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 languageEnglish
Article number9349773
Pages (from-to)30-39
Number of pages10
JournalIEEE Transactions on Consumer Electronics
Issue number1
StatePublished - Feb 2021

Bibliographical note

Funding Information:
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: Digital Object Identifier 10.1109/TCE.2021.3057806

Publisher Copyright:
© 2020 IEEE.


  • 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


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