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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.
Dharmansyah et al. (Mon,) studied this question.