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Smart Health Monitoring Systems: An Overview of Design and Modeling
Abstract
Health monitoring systems have rapidly evolved during the past two decades and have the potential to change the way health care is currently delivered. Although smart health monitoring systems automate patient monitoring tasks and, thereby improve the patient workflow management, their efficiency in clinical settings is still debatable. This paper presents a review of smart health monitoring systems and an overview of their design and modeling. Furthermore, a critical analysis of the efficiency, clinical acceptability, strategies and recommendations on improving current health monitoring systems will be presented. The main aim is to review current state of the art monitoring systems and to perform extensive and an in-depth analysis of the findings in the area of smart health monitoring systems. In order to achieve this, over fifty different monitoring systems have been selected, categorized, classified and compared. Finally, major advances in the system design level have been discussed, current issues facing health care providers, as well as the potential challenges to health monitoring field will be identified and compared to other similar systems.
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“Based on the literature, most monitoring applications which consider home settings or remote monitoring deal predominantly with prediction and anomaly detection whereas the applications in clinical settings are typically focused on diagnosis [10,21].”
From Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges (2013) · cites this paper as [10]
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Metadata
- Authors
- Baig, M. M., & Gholamhosseini, H.
- Affiliations
- Auckland University of Technology (NZ)
- Corresponding author
- Baig, Mirza Mansoor
- Venue
- Journal of Medical Systems
- Published
- 2013-01-15
- Volume
- 37
- Issue
- 2
- Pages
- 9898-9898
- ISSN
- 0148-5598 (ISSN-L)
- 1573-689X
- OpenAlex
- W2019110265
- Cited by
- 396
- References
- 141
- Citations by year
- 2026: 14 · 2025: 12 · 2024: 21 · 2023: 27 · 2022: 26 · 2021: 38 · 2020: 39 · 2019: 52 · 2018: 42 · 2017: 43 · 2016: 30 · 2015: 26 · 2014: 21 · 2013: 5
- FWCI
- 26.97
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Journal of Medical Systems (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Surgery › Healthcare Technology and Patient Monitoring
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Physical Sciences › Computer Science › Computer Networks and Communications › Wireless Sensor Networks for Data Analysis
- Keywords
- patient monitoring, remote patient monitoring, clinical acceptability, system design, system modeling, health care delivery
- MeSH terms
- Confidentiality, Humans, Telemedicine, Monitoring, Ambulatory, Remote Sensing Technology, Wireless Technology
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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.
What counts as a claim ›How we measure confidence ›How we check this ›
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