Leveraging Knowledge From Physiological Data: On-Body Heat Stress Risk Prediction With Sensor Networks
Open accessAbstract
The paper demonstrates that wearable sensor systems, coupled with real-time on-body processing and actuation, can enhance safety for wearers of heavy protective equipment who are subjected to harsh thermal environments by reducing risk of Uncompensable Heat Stress (UHS). The work focuses on Explosive Ordnance Disposal operatives and shows that predictions of UHS risk can be performed in real-time with sufficient accuracy for real-world use. Furthermore, it is shown that the required sensory input for such algorithms can be obtained with wearable, non-intrusive sensors. Two algorithms, one based on Bayesian nets and another on decision trees, are presented for determining the heat stress risk, considering the mean skin temperature prediction as a proxy. The algorithms are trained on empirical data and have accuracies of 92.1±2.9% and 94.4±2.1%, respectively when tested using leave-one-subject-out cross-validation. In applications such as Explosive Ordnance Disposal operative monitoring, such prediction algorithms can enable autonomous actuation of cooling systems and haptic alerts to minimize casualties.
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Anomaly detection techniques are often developed based on classification methods to distinguish normal class and outliers.
“Anomaly detection techniques are often developed based on a classification methods to distinguish the data set into normal class and outliers [26].”
From Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges (2013) · cites this paper as [26]
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 [26]
Bayesian networks, fuzzy state machines, logistic regression models, and association rules have been used for specific health monitoring tasks.
“Some other examples are mentioned here: Bayesian network for classifying stress level [26,39] or for predicting patient's state [72], Fuzzy state machine for estimating the health status to provide alerts [68], logistic regression models [42] and association rules [41] to predict stress level and hospitalization of hemodialysis patients, respectively.”
From Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges (2013) · cites this paper as [26]
Papers that have developed health monitoring systems have mostly used their own data gathering experiments to design, model and test the data analysis step.
“The papers which have developed the health monitoring systems have mostly used their own data gathering experiments to design, model and test the data analysis step [26,38,88].”
From Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges (2013) · cites this paper as [26]
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Flagged first, then by citations
Research works printed but not linked: 3
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Cited by
Sorted by citations
Wearable health devices-vital sign monitoring, systems and technologies
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- Authors
- Gaura, E., Kemp, J., & Brusey, J.
- Affiliations
- Coventry University (GB)
- Venue
- IEEE Transactions on Biomedical Circuits and Systems
- Published
- 2013-12
- Volume
- 7
- Issue
- 6
- Pages
- 861-870
- ISSN
- 1932-4545 (ISSN-L)
- 1940-9990
- OpenAlex
- W1963676251
- Cited by
- 32
- References
- 21
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 1 · 2023: 1 · 2022: 4 · 2021: 1 · 2019: 5 · 2018: 4 · 2017: 3 · 2016: 4 · 2015: 3 · 2014: 3 · 2013: 1
- FWCI
- 2.55
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- IEEE Transactions on Biomedical Circuits and Systems (publishedVersion)
- PubMed (publishedVersion)
- Pure (Coventry University) (submittedVersion, other-oa)
- Coventry University Open Collections (Coventry university) (submittedVersion)
- The Open Collections (Coventry University) (submittedVersion)
- Topics
- Health Sciences › Medicine › Physiology › Thermoregulation and physiological responses
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Keywords
- wearable sensors, real-time physiological monitoring, Bayesian networks, decision tree, explosive ordnance disposal, personal protective equipment, haptic alerts, occupational safety
- MeSH terms
- Adolescent, Adult, Algorithms, Clothing, Equipment Design, Humans, Male, Skin Temperature, Reproducibility of Results, Models, Statistical, Heat Stress Disorders, Decision Support Systems, Clinical, Young Adult, Remote Sensing Technology, Firefighters, Accelerometry
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