The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
A Survey on Wireless Body Area Networks for eHealthcare Systems in Residential Environments
Open accessAbstract
Current progress in wearable and implanted health monitoring technologies has strong potential to alter the future of healthcare services by enabling ubiquitous monitoring of patients. A typical health monitoring system consists of a network of wearable or implanted sensors that constantly monitor physiological parameters. Collected data are relayed using existing wireless communication protocols to a base station for additional processing. This article provides researchers with information to compare the existing low-power communication technologies that can potentially support the rapid development and deployment of WBAN systems, and mainly focuses on remote monitoring of elderly or chronically ill patients in residential environments.
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Citations
Cites
A Comprehensive Survey of Wireless Body Area Networks: On PHY, MAC, and Network Layers Solutions
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Effects of exercise and diet on chronic disease
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DistancePPG: Robust non-contact vital signs monitoring using a camera
Kumar, M., Veeraraghavan, A., & Sabharwal, A. (2015) · Biomedical Optics Express · doi:10.1364/boe.6.001565 · Cited by 417 · cited 2×
“For example, researchers from Rice University have been developing a non-contact video-camera system that can precisely monitor and measure temperature, pulse and breathing rate from changes in a patient’s skin color [99].”
iSleep: unobtrusive sleep quality monitoring using smartphones
Hao, T., Xing, G., & Zhou, G. (2013) · Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems · doi:10.1145/2517351.2517359 · Cited by 216
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“Ryan et al. [105] considered the cost-effectiveness of utilizing smartphone-supported self-monitoring of Asthma.”
Towards the fast and robust optimal design of wireless body area networks
D’Andreagiovanni, F., & Nardin, A. (2015) · Applied Soft Computing · doi:10.1016/j.asoc.2015.04.037 · Cited by 73
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“A few of these articles also investigate specific chronic conditions such as anxiety [112], chronic obstructive pulmonary and chronic kidney [138] and Parkinson’s disease [109,116].”
Cited by
Ghamari, M. (2018) · International Journal of Biosensors & Bioelectronics · doi:10.15406/ijbsbe.2018.04.00125 · Cited by 945
Self-citationA Survey on Mobile Crowdsensing Systems: Challenges, Solutions, and Opportunities
Capponi, A., Fiandrino, C., Kantarci, B., Foschini, L., Kliazovich, D., & Bouvry, P. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2914030 · Cited by 594
Li, J., Herdem, M. S., Nathwani, J., & Wen, J. Z. (2023) · Energy and AI · doi:10.1016/j.egyai.2022.100208 · Cited by 409
Developing residential wireless sensor networks for ECG healthcare monitoring
Dey, N., Ashour, A. S., Shi, F., Fong, S. J., & Sherratt, R. S. (2017) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2017.015063 · Cited by 283
Self-citationAn edge ai-enabled iot healthcare monitoring system for smart cities
Rathi, V. K., Rajput, N. K., Mishra, S., Grover, B. A., Tiwari, P., Jaiswal, A. K., & Hossain, M. S. (2021) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2021.107524 · Cited by 125
“A detailed account of the same was presented in [10], which reports a survey of the advances in Wireless Body Area Network (WBAN) systems and integrated technologies.”
Background
Metadata
- Authors
- Ghamari, M., Janko, B., Sherratt, R., Harwin, W., Piechockic, R., & Soltanpur, C.
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2016-06-07
- Volume
- 16
- Issue
- 6
- Pages
- 831
- ISSN
- 1424-8220 (ISSN-L)
- OpenAlex
- W2412353053
- Cited by
- 300
- References
- 7
- FWCI
- 17.48
- Type
- journal article
- Language
- en
- Open access
- gold
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- Ghamari, M., Janko, B., Sherratt, R., Harwin, W., Piechockic, R., & Soltanpur, C. (2016). A Survey on Wireless Body Area Networks for eHealthcare Systems in Residential Environments. Sensors. https://doi.org/10.3390/s16060831
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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