IoT-based Technologies for Addressing the Unique Healthcare Needs of the Elderly Population
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This paper is a review of the current state of IoT-based technologies in the healthcare field for the elderly population. The increasing population of elderly people has led to a greater demand for healthcare services to meet their unique needs. IoT-based technologies have emerged as a promising solution to address this demand. The paper analyses the current trends, challenges, and future opportunities for research in this area. It highlights how IoT-based technologies can be utilized to improve healthcare for older adults, from remote monitoring and telemedicine to fall detection and medication management. The paper also acknowledges that there are still challenges to be addressed, such as data privacy and security, but the potential benefits for older adults outweigh the challenges. The paper also examines the impact of the industrial revolution on the aging population and the corresponding increase in demand for geriatric healthcare. In summary, this paper delves into the current status of IoT-based technologies in geriatric healthcare and its future prospects, with a focus on addressing the needs of the aging population and improving their quality of life.
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A smart home system with IoT-enabled sensors permits patients with dementia to be monitored online, reducing hospital visits and improving their quality of life.
“A smart home system with IoT-enabled sensors permits patients with dementia to be monitored online, reducing hospital visits and improving their quality of life.”
From Federated Learning in Smart Healthcare: A Comprehensive Review on Privacy, Security, and Predictive Analytics with IoT Integration (2024) · cites this paper as [97]
Section 4.5 describes an IoT care system with a smart-home sensor that tracks a patient's location and alerts a caretaker if the patient wanders off; section 4.6 also discusses smart-home sensors for in-house monitoring. These passages support sensor-based home monitoring, but do not establish that it reduces hospital visits or improves quality of life for patients with dementia. (LLM, conf=0.83)
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Abbas, S. R., Abbas, Z., Zahir, A., & Lee, S. W. (2024) · Healthcare · doi:10.3390/healthcare12242587 · Cited by 228 · see citing works
- Authors
- Ali, S. A., & Khan, R.
- Affiliations
- United Arab Emirates University (AE)
- Corresponding author
- Ali, Syed Adnan
- ORCID
- Khan, Rehan
- Venue
- International Journal of Artificial Intelligence and Machine Learning
- Publisher
- MDPI AG
- Published
- 2023-03-06
- Volume
- 4
- Issue
- 1
- Pages
- 94-121
- ISSN
- 2789-2557 (ISSN-L)
- OpenAlex
- W4391695399
- Cited by
- 7
- References
- 230
- Citations by year
- 2025: 3 · 2024: 3 · 2023: 1
- FWCI
- 2.25
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- International Journal of Artificial Intelligence and Machine Learning (publishedVersion)
- Preprints.org (acceptedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Social Sciences › Demography › Technology Use by Older Adults
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Keywords
- Internet of Things, elderly healthcare, elderly care, remote patient monitoring, telemedicine, fall detection, medication management, data privacy, data security, population aging, quality of life
About these checks
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.
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