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Prognosis—A Wearable Health-Monitoring System for People at Risk: Methodology and Modeling
Abstract
Wearable health-monitoring systems (WHMSs) represent the new generation of healthcare by providing real-time unobtrusive monitoring of patients' physiological parameters through the deployment of several on-body and even intrabody biosensors. Although several technological issues regarding WHMS still need to be resolved in order to become more applicable in real-life scenarios, it is expected that continuous ambulatory monitoring of vital signs will enable proactive personal health management and better treatment of patients suffering from chronic diseases, of the elderly population, and of emergency situations. In this paper, we present a physiological data fusion model for multisensor WHMS called Prognosis. The proposed methodology is based on a fuzzy regular language for the generation of the prognoses of the health conditions of the patient, whereby the current state of the corresponding fuzzy finite-state machine signifies the current estimated health state and context of the patient. The operation of the proposed scheme is explained via detailed examples in hypothetical scenarios. Finally, a stochastic Petri net model of the human-device interaction is presented, which illustrates how additional health status feedback can be obtained from the WHMS' user.
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“Some examples of recent works in this area involve estimating the severity of health episodes of patients suffering chronic disease [44–46], sleep issues such as polysomnography and apnea [47,48], estimation and classification of health conditions [49,50], and emotion recognition [51].”
From Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges (2013) · cites this paper as [50]
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Metadata
- Authors
- Pantelopoulos, A., & Bourbakis, N. G.
- Affiliations
- Wright State University (US)
- Venue
- IEEE Transactions on Information Technology in Biomedicine
- Published
- 2010-05
- Volume
- 14
- Issue
- 3
- Pages
- 613-621
- ISSN
- 1089-7771 (ISSN-L)
- OpenAlex
- W2101305037
- Cited by
- 136
- References
- 46
- Citations by year
- 2026: 7 · 2025: 6 · 2024: 6 · 2023: 10 · 2022: 6 · 2021: 11 · 2020: 6 · 2019: 7 · 2018: 7 · 2017: 10 · 2016: 6 · 2015: 11 · 2014: 15 · 2013: 15 · 2012: 5
- FWCI
- 5.29
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Information Technology in Biomedicine (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Computer Science › Computer Networks and Communications › Wireless Sensor Networks for Data Analysis
- Keywords
- sensor fusion, fuzzy finite state machines, state of health estimation, stochastic Petri nets, human-device interaction, chronic disease monitoring, elderly monitoring, emergency situations, personal health management
- MeSH terms
- Blood Pressure, Clothing, Electrocardiography, Humans, Programming Languages, Risk Factors, Signal Processing, Computer-Assisted, Stochastic Processes, Fuzzy Logic, Monitoring, Ambulatory, Decision Support Systems, Clinical, Vital Signs
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