The DOI matches its Crossref record; 46 of the 46 references it cites were found in a registry or an index.
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]
Citations
Cites
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A Survey on Wearable Sensor-Based Systems for Health Monitoring and Prognosis
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Automatic Classification of Heartbeats Using ECG Morphology and Heartbeat Interval Features
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Bioelectrical Signal Processing in Cardiac and Neurological Applications
Sörnmo, L., & Laguna, P. (2005) · Elsevier eBooks · doi:10.1016/b978-0-12-437552-9.x5000-4 · Cited by 1,003
Postural stability in the elderly: a comparison between fallers and non-fallers
Melzer, I. (2004) · Age and Ageing · doi:10.1093/ageing/afh218 · Cited by 736
Sensor networks for medical care
Shnayder, V., Chen, B.-R., Lorincz, K., Fulford-Jones, T. R. F., & Welsh, M. (2005) · ACM Conference on Embedded Networked Sensor Systems (SenSys) · doi:10.1145/1098918.1098979 · Cited by 723
AMON: A Wearable Multiparameter Medical Monitoring and Alert System
Anliker, U., Ward, J. A., Lukowicz, P., Troster, G., Dolveck, F., Baer, M., Keita, F., Schenker, E. B., Catarsi, F., Coluccini, L., et al. (2004) · IEEE Transactions on Information Technology in Biomedicine · doi:10.1109/titb.2004.837888 · Cited by 707
Wireless body sensor networks for health-monitoring applications
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Research works printed but not linked: 2
- kalb, 2006, fixing america's hospitals, Newsweek Mag, 148, 44
- 10.1109/IEMBS.2007.4353296 · DOI
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Banaee, H., Ahmed, M., & Loutfi, A. (2013) · Sensors · doi:10.3390/s131217472 · Cited by 452
Smart health monitoring systems: An overview of design and modeling
Baig, M. M., & Gholamhosseini, H. (2013) · Journal of Medical Systems · doi:10.1007/s10916-012-9898-z · Cited by 396
Graphene-Based Sensors for Human Health Monitoring
Huang, H., Su, S., Wu, N., Wan, H., Wan, S., Bi, H., & Sun, L. (2019) · Frontiers in Chemistry · doi:10.3389/fchem.2019.00399 · Cited by 343
Shameer, K., Badgeley, M. A., Miotto, R., Glicksberg, B. S., Morgan, J. W., & Dudley, J. T. (2017) · Briefings in Bioinformatics · doi:10.1093/bib/bbv118 · Cited by 198
Lanatà, A., Valenza, G., Nardelli, M., Gentili, C., & Scilingo, E. P. (2014) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2014.2360711 · Cited by 168
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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