A Survey on Wearable Sensor-Based Systems for Health Monitoring and Prognosis
Abstract
The design and development of wearable biosensor systems for health monitoring has garnered lots of attention in the scientific community and the industry during the last years. Mainly motivated by increasing healthcare costs and propelled by recent technological advances in miniature biosensing devices, smart textiles, microelectronics, and wireless communications, the continuous advance of wearable sensor-based systems will potentially transform the future of healthcare by enabling proactive personal health management and ubiquitous monitoring of a patient's health condition. These systems can comprise various types of small physiological sensors, transmission modules and processing capabilities, and can thus facilitate low-cost wearable unobtrusive solutions for continuous all-day and any-place health, mental and activity status monitoring. This paper attempts to comprehensively review the current research and development on wearable biosensor systems for health monitoring. A variety of system implementations are compared in an approach to identify the technological shortcomings of the current state-of-the-art in wearable biosensor solutions. An emphasis is given to multiparameter physiological sensing system designs, providing reliable vital signs measurements and incorporating real-time decision support for early detection of symptoms or context awareness. In order to evaluate the maturity level of the top current achievements in wearable health-monitoring systems, a set of significant features, that best describe the functionality and the characteristics of the systems, has been selected to derive a thorough study. The aim of this survey is not to criticize, but to serve as a reference for researchers and developers in this scientific area and to provide direction for future research improvements.
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Wearable devices can provide real-time feedback regarding a person's health conditions and thus provide an objective alternative to manage and monitor chronic disease progression in the elderly, within rehabilitation, and for those with various disabilities.
“Wearable devices can provide real-time feedback regarding a person’s health conditions; hence, they can provide an objective alternative to manage and monitor chronic disease progression, such as with the elderly, within rehabilitation, and for those with various disabilities [1].”
From Review of Wearable Devices and Data Collection Considerations for Connected Health (2021) · cites this paper as [1]
Diagnosis/decision making is related to anomaly detection in data mining to extract useful information from sensor data such as events, outliers, and alarms meaningful for decision.
“The diagnosis/decision making is related to the anomaly detection in data mining in order to extract useful information of sensor data such as events, outliers, and alarms which are meaningful for decision [8].”
From Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges (2013) · cites this paper as [8]
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Wang, H., Li, J., McDonald, B. E., Farrell, T. R., Huang, X., & Clancy, E. A. (2023) · Sensors · doi:10.3390/s23052465 · Cited by 18 · see citing works
Jangir, M. K., & Singh, K. (2019) · Journal of Discrete Mathematical Sciences and Cryptography · doi:10.1080/09720529.2019.1696552 · Cited by 11 · see citing works
Huang, H., Hu, S., & Sun, Y. (2019) · ACM Transactions on Cyber-Physical Systems · doi:10.1145/3341559 · Cited by 10 · see citing works
Vijayan, V., McKelvey, N., Condell, J., Gardiner, P., & Connolly, J. (2020) · 2020 31st Irish Signals and Systems Conference (ISSC) · doi:10.1109/issc49989.2020.9180174 · Cited by 3 · see citing works
Prasath, N., Arun, A., Saravanan, B., & Kamaraj, K. (2025) · Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology · doi:10.3233/jifs-233495 · Cited by 3 · see citing works
Retracted
- Authors
- Pantelopoulos, A., & Bourbakis, N. G.
- Affiliations
- Wright State University (US)
- ORCID
- Bourbakis, N.G.
- Venue
- IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews)
- Published
- 2010-01
- Volume
- 40
- Issue
- 1
- Pages
- 1-12
- ISSN
- 1094-6977 (ISSN-L)
- 1558-2442
- OpenAlex
- W2131496831
- Cited by
- 2,296
- References
- 60
- Citations by year
- 2026: 92 · 2025: 113 · 2024: 97 · 2023: 112 · 2022: 111 · 2021: 164 · 2020: 167 · 2019: 203 · 2018: 211 · 2017: 242 · 2016: 200 · 2015: 182 · 2014: 144 · 2013: 104 · 2012: 80
- FWCI
- 30.70
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews) (publishedVersion)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Physical Sciences › Engineering › Biomedical Engineering › Wireless Body Area Networks
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Keywords
- wearable biosensors, health monitoring, prognosis, physiological sensing, multi-parameter sensing, vital signs, real-time decision support, context awareness, smart textiles, wireless health monitoring, personal health management, early symptom detection
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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