Stress monitoring using wearable sensors: a pilot study and stress-predict dataset

Talha Iqbal*, Andrew J. Simpkin, Davood Roshan, Nicola Glynn, John Killilea, Jane Walsh, Gerard Molloy, Sandra Ganly, Hannah Ryman, Eileen Coen, Adnan Elahi, William Wijns, Atif Shahzad

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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Abstract

With the recent advancements in the field of wearable technologies, the opportunity to monitor stress continuously using different physiological variables has gained significant interest. The early detection of stress can help improve healthcare and minimizes the negative impact of long-term stress. This paper reports outcomes of a pilot study and associated stress-monitoring dataset, named the “Stress-Predict Dataset”, created by collecting physiological signals from healthy subjects using wrist-worn watches with a photoplethysmogram (PPG) sensor. While wearing these watches, 35 healthy volunteers underwent a series of tasks (i.e., Stroop color test, Trier Social Stress Test and Hyperventilation Provocation Test), along with a rest period in-between each task. They also answered questionnaires designed to induce stress levels compatible with daily life. The changes in the blood volume pulse (BVP) and heart rate were recorded by the watch and were labelled as occurring during stress-inducing tasks or a rest period (no stress). Additionally, respiratory rate was estimated using the BVP signal. Statistical models and personalised adaptive reference ranges were used to determine the utility of the proposed stressors and the extracted variables (heart rate and respiratory rate). The analysis showed that the interview session was the most significant stress stimulus, causing a significant variation in heart rate of 27 (77%) participants and respiratory rate of 28 (80%) participants out of 35. The outcomes of this study contribute to the understanding the role of stressors and their association with physiological response and provide a dataset to help develop new wearable solutions for more reliable, valid, and sensitive physio-logical stress monitoring.

Original languageEnglish
Article number8135
Number of pages16
JournalSensors
Volume22
Issue number21
DOIs
Publication statusPublished - 24 Oct 2022

Bibliographical note

Funding Information:
This publication has emanated from research conducted with the financial support of Science Foundation Ireland under Research Professorship to W.W. [Grant number 15/RP/2765]. For Open Access, the author has applied a CC BY public copyright license to any Author-Agreed Manuscript version arising from this submission. A.S. acknowledges financial support from the University of Birmingham Dynamic Investment Fund. A.J.S. is supported by Science Foundation Ireland under Grant number 19/FFP/7002.

Publisher Copyright:
© 2022 by the authors.

Keywords

  • adaptive reference ranges
  • biomedical signal processing
  • health monitoring
  • heart rate
  • non-invasive devices
  • photoplethysmogram (PPG)
  • respiratory rate
  • stress-predict dataset

ASJC Scopus subject areas

  • Analytical Chemistry
  • Information Systems
  • Biochemistry
  • Atomic and Molecular Physics, and Optics
  • Instrumentation
  • Electrical and Electronic Engineering

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