Remote Health Monitoring Systems for Elderly People: A Survey
Open accessAbstract
This paper addresses the growing demand for healthcare systems, particularly among the elderly population. The need for these systems arises from the desire to enable patients and seniors to live independently in their homes without relying heavily on their families or caretakers. To achieve substantial improvements in healthcare, it is essential to ensure the continuous development and availability of information technologies tailored explicitly for patients and elderly individuals. The primary objective of this study is to comprehensively review the latest remote health monitoring systems, with a specific focus on those designed for older adults. To facilitate a comprehensive understanding, we categorize these remote monitoring systems and provide an overview of their general architectures. Additionally, we emphasize the standards utilized in their development and highlight the challenges encountered throughout the developmental processes. Moreover, this paper identifies several potential areas for future research, which promise further advancements in remote health monitoring systems. Addressing these research gaps can drive progress and innovation, ultimately enhancing the quality of healthcare services available to elderly individuals. This, in turn, empowers them to lead more independent and fulfilling lives while enjoying the comforts and familiarity of their own homes. By acknowledging the importance of healthcare systems for the elderly and recognizing the role of information technologies, we can address the evolving needs of this population. Through ongoing research and development, we can continue to enhance remote health monitoring systems, ensuring they remain effective, efficient, and responsive to the unique requirements of elderly individuals.
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Sensors in smart homes can detect falls, monitor sleep patterns, and even track medication regularization, helping patients to keep independence while confirming timely interventions when required.
“Sensors in smart homes can detect falls, monitor sleep patterns, and even track medication regularization, helping patients to keep independence while confirming timely interventions when required [96].”
From Federated Learning in Smart Healthcare: A Comprehensive Review on Privacy, Security, and Predictive Analytics with IoT Integration (2024) · cites this paper as [96]
Section 3, “Human Activity Recognition in RHMS” (page unavailable), describes sensor-based recognition of falls and sleeping, but does not establish monitoring of sleep patterns. The Introduction and Section 4 describe prompt caregiver alerts and support for independent living at home. The copy does not support the medication-regularity part of the claim (searched: medication, medication adherence). (LLM, conf=0.87)
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Infrastructure-less Occupancy Detection and Semantic Localization in Smart Environments
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HRIDaaY: Ballistocardiogram-Based Heart Rate Monitoring Using Fog Computing
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Cited by
Sorted by citations
Abbas, S. R., Abbas, Z., Zahir, A., & Lee, S. W. (2024) · Healthcare · doi:10.3390/healthcare12242587 · Cited by 228 · see citing works
Smart Speakers for Health and Well-Being of Older Adults: A Mixed-Methods Review
Dino, M. J., Leinbach, C., Dino, G., Thiamwong, L., Villafuerte, C. M., Shattell, M., Pimentel, J., Zamora, M. A., Bautista, A., Vitug, J. P., et al. (2025) · Healthcare · doi:10.3390/healthcare13212772 · Cited by 5 · see citing works
- Authors
- Ahmed, S., Irfan, S., Kiran, N., Masood, N., Anjum, N., & Ramzan, N.
- Affiliations
- Capital University of Science and Technology (PK)
- Department of Information Engineering Technology, National Skills University, Islamabad 44000, Pakistan
- University of the West of Scotland (GB)
- Corresponding author
- Ahmed, Salman
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2023-08-10
- Volume
- 23
- Issue
- 16
- Pages
- 7095
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC10458487
- OpenAlex
- W4385741170
- Cited by
- 52
- References
- 114
- Citations by year
- 2026: 12 · 2025: 23 · 2024: 16 · 2023: 1
- FWCI
- 1.82
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- The UWS Academic Portal (University of the West of Scotland) (acceptedVersion, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Zenodo (CERN European Organization for Nuclear Research) (submittedVersion, cc-by)
- Funders
- Erasmus plus Capacity Building in Higher Education (CBHE) programme (SAFE-RH Project-619483-EPP-1-2020-1-UK-EPPKA2-CBHE-JP)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Wireless Sensor Networks for Data Analysis
- Social Sciences › Demography › Technology Use by Older Adults
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Keywords
- remote patient monitoring, elderly care, older adults, home care, independent living, monitoring system architecture, health care standards, population aging
- MeSH terms
- Information Technology, Evidence Gaps, Aged, Humans, Recognition, Psychology
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.
What counts as a claim ›How we measure confidence ›How we check this ›
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