The DOI matches its Crossref record; 41 of the 41 references it cites were found in a registry or an index.
Remote health monitoring of elderly through wearable sensors
Open accessAbstract
Due to a rapidly increasing aging population and its associated challenges in health and social care, Ambient Assistive Living has become the focal point for both researchers and industry alike. The need to manage or even reduce healthcare costs while improving the quality of service is high government agendas. Although, technology has a major role to play in achieving these aspirations, any solution must be designed, implemented and validated using appropriate domain knowledge. In order to overcome these challenges, the remote real-time monitoring of a person’s health can be used to identify relapses in conditions, therefore, enabling early intervention. Thus, the development of a smart healthcare monitoring system, which is capable of observing elderly people remotely, is the focus of the research presented in this paper. The technology outlined in this paper focuses on the ability to track a person’s physiological data to detect specific disorders which can aid in Early Intervention Practices. This is achieved by accurately processing and analysing the acquired sensory data while transmitting the detection of a disorder to an appropriate career. The finding reveals that the proposed system can improve clinical decision supports while facilitating Early Intervention Practices. Our extensive simulation results indicate a superior performance of the proposed system: low latency (96% of the packets are received with less than 1 millisecond) and low packets-lost (only 2.2% of total packets are dropped). Thus, the system runs efficiently and is cost-effective in terms of data acquisition and manipulation.
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Cites
The Internet of Things for Health Care: A Comprehensive Survey
Riazul Islam, S. M., Kwak, D., Humaun Kabir, M., Hossain, M., & Kwak, K.-S. (2015) · IEEE Access · doi:10.1109/access.2015.2437951 · Cited by 3,095
A review of wearable sensors and systems with application in rehabilitation
Patel, S., Park, H., Bonato, P., Chan, L., & Rodgers, M. (2012) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/1743-0003-9-21 · Cited by 2,396
A Survey on Wearable Sensor-Based Systems for Health Monitoring and Prognosis
Pantelopoulos, A., & Bourbakis, N. G. (2010) · IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews) · doi:10.1109/tsmcc.2009.2032660 · Cited by 2,296
TinyDB: an acquisitional query processing system for sensor networks
Madden, S. R., Franklin, M. J., Hellerstein, J. M., & Hong, W. (2005) · ACM Transactions on Database Systems · doi:10.1145/1061318.1061322 · Cited by 1,899
An IoT-Aware Architecture for Smart Healthcare Systems
Catarinucci, L., de Donno, D., Mainetti, L., Palano, L., Patrono, L., Stefanizzi, M. L., & Tarricone, L. (2015) · IEEE Internet of Things Journal · doi:10.1109/jiot.2015.2417684 · Cited by 1,167
Hassanalieragh, M., Page, A., Soyata, T., Sharma, G., Aktas, M., Mateos, G., Kantarci, B., & Andreescu, S. (2015) · 2015 IEEE International Conference on Services Computing · doi:10.1109/scc.2015.47 · Cited by 787
Sensor networks for medical care
Shnayder, V., Chen, B.-R., Lorincz, K., Jones, T. R. F. F., & Welsh, M. (2005) · Proceedings of the 3rd international conference on Embedded networked sensor systems · doi:10.1145/1098918.1098979 · Cited by 720
The Internet of Things for Ambient Assisted Living
Dohr, A., Modre-Opsrian, R., Drobics, M., Hayn, D., & Schreier, G. (2010) · 2010 Seventh International Conference on Information Technology: New Generations · doi:10.1109/itng.2010.104 · Cited by 553
Dall, T. M., Gallo, P. D., Chakrabarti, R., West, T., Semilla, A. P., & Storm, M. V. (2013) · Health Affairs · doi:10.1377/hlthaff.2013.0714 · Cited by 529
Smart Environments: Technology, Protocols and Applications
(2004) · Cited by 472
Cited by
Chandrasekaran, R., Katthula, V., & Moustakas, E. (2021) · Health Informatics Journal · doi:10.1177/14604582211058073 · Cited by 90
Remote Health Monitoring Systems for Elderly People: A Survey
Ahmed, S., Irfan, S., Kiran, N., Masood, N., Anjum, N., & Ramzan, N. (2023) · Sensors · doi:10.3390/s23167095 · Cited by 52
Evaluating Internet of Medical Things (IoMT)-Based Systems from a Human-Centric Perspective
Kotronis, C., Routis, I., Politi, E., Nikolaidou, M., Dimitrakopoulos, G., Anagnostopoulos, D., Amira, A., Bensaali, F., & Djelouat, H. (2019) · Internet of Things · doi:10.1016/j.iot.2019.100125 · Cited by 50
HCM-Dynamic-Echo: A Framework for Detecting Hypertrophic Cardiomyopathy (HCM) in Echocardiograms
Almadani, A., Agu, E., Sarwar, A., Ahluwalia, M., & Kpodonu, J. (2023) · 2023 IEEE International Conference on Digital Health (ICDH) · doi:10.1109/icdh60066.2023.00039 · Cited by 5
Context-Aware Remote Patient Monitoring System with IoT and Digital Security
Geetha, S., Karthikeyan, P., N, Y., D, P., & V, V. (2024) · 2024 International Conference on System, Computation, Automation and Networking (ICSCAN) · doi:10.1109/icscan62807.2024.10894392 · Cited by 1
Metadata
- Authors
- Al-khafajiy, M., Baker, T., Chalmers, C., Asim, M., Kolivand, H., Fahim, M., & Waraich, A.
- Venue
- Multimedia Tools and Applications
- Published
- 2019-01-24
- Volume
- 78
- Issue
- 17
- Pages
- 24681-24706
- ISSN
- 1380-7501 (ISSN-L)
- 1573-7721
- OpenAlex
- W2913066864
- Cited by
- 365
- References
- 41
- FWCI
- 124.62
- Type
- book chapter
- Language
- en
- Open access
- hybrid
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