ReviewJournal of multidisciplinary healthcare2025
Bibliometric Analysis of Research Articles on Embedded Internet of Health Things (IoHT) Fall Detection in the Elderly Published from 2006 to 2025.
Review in Journal of multidisciplinary healthcare, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Voices from Primary Care: A Needs Assessment Among Multidisciplinary Primary-Care Teams for a Health Promotion Model-Based E-Counseling Platform to Support Fall Prevention Among Older Adults in Indonesia.Journal of multidisciplinary healthcare · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Falls are a major cause of injury and death among the elderly, highlighting the need for effective and real-time detection systems. Embedded Internet of Health Things (IoHT) technologies integrating sensors, microcontrollers, and communication modules offer continuous monitoring and rapid response. However, the research landscape remains fragmented, and no comprehensive bibliometric review has been conducted. Methods: This study presents a bibliometric analysis of articles on embedded IoHT-based fall detection for the elderly, indexed in Scopus from 2006 to April 2025. Keywords related to embedded systems, IoHT, fall detection, and elderly care were used. Data extracted included publication year, type, country, institution, author, citations, and methodology. Analysis included performance metrics, science mapping, and visualization using VOSviewer. Results: A total of 79 publications were found, mostly conference papers (55.7%) and journal articles (39.2%). Research activity increased notably after 2018, peaking in 2024. India, China, and the United States led in volume, while Spain and Ecuador produced highly cited works, especially on wearable and IoT-based solutions. Thematic analysis revealed four clusters: ambient intelligence, inertial sensors, demographic personalization, and networked wearables linked to telemedicine. Methodologies evolved from threshold-based to advanced AI, including machine learning, deep learning, and edge/cloud integration. Key gaps remain in standard datasets, privacy, and equitable demographic representation. Conclusion: Embedded IoHT fall detection research has grown rapidly, shifting toward AI-driven, clinically integrated, and user-centered systems. Future efforts should focus on standardization, privacy frameworks, and inclusive design to ensure broader real-world application in elderly care.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.