Development and Evaluation of an Ambulatory Stress Monitor Based on Wearable Sensors
Abstract
Chronic stress is endemic to modern society. However, as it is unfeasible for physicians to continuously monitor stress levels, its diagnosis is nontrivial. Wireless body sensor networks offer opportunities to ubiquitously detect and monitor mental stress levels, enabling improved diagnosis, and early treatment. This article describes the development of a wearable sensor platform to monitor a number of physiological correlates of mental stress. We discuss tradeoffs in both system design and sensor selection to balance information content and wearability. Using experimental signals collected from the wearable sensor, we describe a selected number of physiological features that show good correlation with mental stress. In particular, we propose a new spectral feature that estimates the balance of the autonomic nervous system by combining information from the power spectral density of respiration and heart rate variability. We validate the effectiveness of our approach on a binary discrimination problem when subjects are placed under two psychophysiological conditions: mental stress and relaxation. When used in a logistic regression model, our feature set is able to discriminate between these two mental states with a success rate of 81% across subjects.
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Several new prediction works have used experimental wearable sensor data to perform non-clinical health monitoring.
“However, there are several new prediction works which have used experimental wearable sensor data to perform non clinical health monitoring [26,42].”
From Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges (2013) · cites this paper as [42]
Since wearable sensor data for vital sign parameters tend to be continuous time series readings, most considered features are related to properties of time series signals.
“Since Wearable sensor data which provide monitoring of vital sign parameters tend to be continuous time series readings, most of the considered features are related to the properties of time series signals [42].”
From Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges (2013) · cites this paper as [42]
Among ANS responses, skin temperature, skin conductance, and arterial pulsewave signals are required for comprehensive multimodal physiological data analysis.
“Among ANS responses, the skin temperature1126, skin conductance112628, and arterial pulsewave11232628 signals are required for the comprehensive multimodal physiological data analysis.”
From A Flexible and Wearable Human Stress Monitoring Patch (2016) · cites this paper as [28]
The skin contact area of the fabricated multi-layer patch (3.71 cm2) is as small as 1/125 of that of the conventional single layer multimodal stress monitoring device (451 cm2).
“As a result, the skin contact area of the fabricated multi-layer patch (3.71 cm2) is as small as the 1/125 of that of the conventional single layer multimodal stress monitoring device (451 cm2)28.”
From A Flexible and Wearable Human Stress Monitoring Patch (2016) · cites this paper as [28]
Due to the nature of vital signs, some are essentially counted as continuous such as ECG, heart rate and respiration rate, or discrete like blood glucose level.
“Due to the nature of vital signs, some of them are essentially counted as continuous such as ECG, heart rate and respiration rate [31,42] or discrete like blood glucose level [28].”
From Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges (2013) · cites this paper as [42]
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- Authors
- Choi, J., Ahmed, B., & Gutierrez-Osuna, R.
- Affiliations
- Texas A&M University (US)
- Texas A&M University at Qatar (QA)
- ORCID
- Ahmed, B.
- Venue
- IEEE Transactions on Information Technology in Biomedicine
- Published
- 2012-03
- Volume
- 16
- Issue
- 2
- Pages
- 279-286
- ISSN
- 1089-7771 (ISSN-L)
- 1558-0032
- OpenAlex
- W2050284630
- Semantic Scholar
- 74fb3a4c124a20fd2f3f31510bd146ace378f30e
- Cited by
- 221
- References
- 53
- Citations by year
- 2026: 6 · 2025: 15 · 2024: 21 · 2023: 15 · 2022: 16 · 2021: 21 · 2020: 25 · 2019: 21 · 2018: 10 · 2017: 14 · 2016: 15 · 2015: 16 · 2014: 9 · 2013: 13 · 2012: 4
- FWCI
- 10.75
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Information Technology in Biomedicine (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Heart Rate Variability and Autonomic Control
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Keywords
- wearable sensors, wireless body sensor networks, sympathovagal balance, heart rate variability, respiration, power spectral density, physiological traits, stress detection, logistic regression classification
- MeSH terms
- Clothing, Electric Conductivity, Electromyography, Equipment Design, Heart Rate, Humans, Models, Biological, Relaxation, Respiration, Signal Processing, Computer-Assisted, Skin Physiological Phenomena, Stress, Psychological, Reproducibility of Results, Logistic Models, Monitoring, Ambulatory
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