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Visual Intelligence for Automated Fall Sensing: A Systematic Review of Architectures, Datasets, and Evaluation Gaps
Review Article  ·  Published: 27 June 2026
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ICCK Transactions on Sensing, Communication, and Control
Volume 3, Issue 2, 2026: 90-108
Review Article Free to Read

Visual Intelligence for Automated Fall Sensing: A Systematic Review of Architectures, Datasets, and Evaluation Gaps

by
Babar Zeb Babar Zeb 1,† ORCID
Muhammad Talha Usman Muhammad Talha Usman 2,† ORCID
Habib Khan Habib Khan 2,† * ORCID
Alexandros Gazis Alexandros Gazis 3,† ORCID
Stylianos Pappas Stylianos Pappas 4,† ORCID
Muhammad Faizan Omer Muhammad Faizan Omer 5,† ORCID
Nasir Rahim Nasir Rahim 2,† * ORCID
1 Department of Computer and Software Engineering, College of Electrical and Mechanical Engineering, NUST, Islamabad, Pakistan
2 School of Computing, Gachon University, Seongnam-si 13120, Republic of Korea
3 Department of Electrical and Computer Engineering, Democritus University of Thrace, Xanthi 67100, Greece
4 Electrical Engineering and Computer Science, Hellenic Naval Academy, Terma Chatzikyriakou, Piraeus 18539, Greece
5 Department of Computer Software Engineering, Military College of Signals, National University of Sciences and Technology (NUST), Rawalpindi, Pakistan
† These authors contributed equally to this work
* Corresponding Authors: Habib Khan, [email protected]; Nasir Rahim, [email protected]
Volume 3, Issue 2
Volume 3, Issue 2 2026
Received: 16 November 2024 Accepted: 20 May 2026 Published: 27 June 2026 CrossMark
Download PDF 7.86 MB Cite Metrics
DOI: 10.62762/TSCC.2026.604481
ARK: ark:/57805/tscc.2026.604481

Article Information

Published in ICCK Transactions on Sensing, Communication, and Control
Volume/Issue Volume 3, Issue 2, 2026
Pages 90-108

Abstract

Falls are a major cause of injury, hospitalization, and loss of independence among older adults, spurring interest in visual intelligence-based automated fall detection for timely response and continuous monitoring. This article presents a systematic review of such systems, focusing on YOLO-based approaches. Following PRISMA guidelines, the review covers 2016–2025 literature, identifying 637 records and including 63 studies after screening. We examine datasets, preprocessing strategies, evaluation protocols, metrics, and hardware platforms, comparing reported accuracy, efficiency, and real-time feasibility across different designs. Evidence is strongest for YOLOv3 through YOLOv9, while evidence for YOLOv1–YOLOv2 and YOLOv10–YOLOv12 remains limited or preliminary. Reviewed studies show steady performance gains on public datasets, but key limitations persist: most evaluations use staged or lab-controlled data, cross-dataset and cross-site testing are uncommon, and deployment factors like end-to-end latency, edge device performance, and long-term stability are under-reported. This review consolidates current practices and identifies priorities for more standardized, deployment-relevant reporting.

Graphical Abstract

Visual Intelligence for Automated Fall Sensing: A Systematic Review of Architectures, Datasets, and Evaluation Gaps

Keywords

fall detection visual monitoring object detection systematic review edge devices real-time assisted living

Data Availability Statement

Not applicable.

Funding

This work was supported without any funding.

Conflicts of Interest

Habib Khan served as an Associate Editor of the ICCK Transactions on Sensing, Communication, and Control at the time of manuscript submission. To ensure the integrity of the peer-review process, Habib Khan was not involved in the editorial handling, peer review, or decision-making process for this manuscript, which was handled independently by another editor. The remaining authors declare no conflicts of interest.

AI Use Statement

The authors declare that no generative AI was used in the preparation of this manuscript.

Ethical Approval and Consent to Participate

Not applicable.

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Cite This Article

APA Style
Zeb, B., Usman, M. T., Khan, H., Gazis, A., Pappas, S., Omer, M. F., & Rahim, N. (2026). Visual Intelligence for Automated Fall Sensing: A Systematic Review of Architectures, Datasets, and Evaluation Gaps. ICCK Transactions on Sensing, Communication, and Control, 3(2), 90-108. https://doi.org/10.62762/TSCC.2026.604481
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TY  - JOUR
AU  - Zeb, Babar
AU  - Usman, Muhammad Talha
AU  - Khan, Habib
AU  - Gazis, Alexandros
AU  - Pappas, Stylianos
AU  - Omer, Muhammad Faizan
AU  - Rahim, Nasir
PY  - 2026
DA  - 2026/06/27
TI  - Visual Intelligence for Automated Fall Sensing: A Systematic Review of Architectures, Datasets, and Evaluation Gaps
JO  - ICCK Transactions on Sensing, Communication, and Control
T2  - ICCK Transactions on Sensing, Communication, and Control
JF  - ICCK Transactions on Sensing, Communication, and Control
VL  - 3
IS  - 2
SP  - 90
EP  - 108
DO  - 10.62762/TSCC.2026.604481
UR  - https://www.icck.org/article/abs/TSCC.2026.604481
KW  - fall detection
KW  - visual monitoring
KW  - object detection
KW  - systematic review
KW  - edge devices
KW  - real-time
KW  - assisted living
AB  - Falls are a major cause of injury, hospitalization, and loss of independence among older adults, spurring interest in visual intelligence-based automated fall detection for timely response and continuous monitoring. This article presents a systematic review of such systems, focusing on YOLO-based approaches. Following PRISMA guidelines, the review covers 2016–2025 literature, identifying 637 records and including 63 studies after screening. We examine datasets, preprocessing strategies, evaluation protocols, metrics, and hardware platforms, comparing reported accuracy, efficiency, and real-time feasibility across different designs. Evidence is strongest for YOLOv3 through YOLOv9, while evidence for YOLOv1–YOLOv2 and YOLOv10–YOLOv12 remains limited or preliminary. Reviewed studies show steady performance gains on public datasets, but key limitations persist: most evaluations use staged or lab-controlled data, cross-dataset and cross-site testing are uncommon, and deployment factors like end-to-end latency, edge device performance, and long-term stability are under-reported. This review consolidates current practices and identifies priorities for more standardized, deployment-relevant reporting.
SN  - 3068-9287
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  -
BibTeX Format
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@article{Zeb2026Visual,
  author = {Babar Zeb and Muhammad Talha Usman and Habib Khan and Alexandros Gazis and Stylianos Pappas and Muhammad Faizan Omer and Nasir Rahim},
  title = {Visual Intelligence for Automated Fall Sensing: A Systematic Review of Architectures, Datasets, and Evaluation Gaps},
  journal = {ICCK Transactions on Sensing, Communication, and Control},
  year = {2026},
  volume = {3},
  number = {2},
  pages = {90-108},
  doi = {10.62762/TSCC.2026.604481},
  url = {https://www.icck.org/article/abs/TSCC.2026.604481},
  abstract = {Falls are a major cause of injury, hospitalization, and loss of independence among older adults, spurring interest in visual intelligence-based automated fall detection for timely response and continuous monitoring. This article presents a systematic review of such systems, focusing on YOLO-based approaches. Following PRISMA guidelines, the review covers 2016–2025 literature, identifying 637 records and including 63 studies after screening. We examine datasets, preprocessing strategies, evaluation protocols, metrics, and hardware platforms, comparing reported accuracy, efficiency, and real-time feasibility across different designs. Evidence is strongest for YOLOv3 through YOLOv9, while evidence for YOLOv1–YOLOv2 and YOLOv10–YOLOv12 remains limited or preliminary. Reviewed studies show steady performance gains on public datasets, but key limitations persist: most evaluations use staged or lab-controlled data, cross-dataset and cross-site testing are uncommon, and deployment factors like end-to-end latency, edge device performance, and long-term stability are under-reported. This review consolidates current practices and identifies priorities for more standardized, deployment-relevant reporting.},
  keywords = {fall detection, visual monitoring, object detection, systematic review, edge devices, real-time, assisted living},
  issn = {3068-9287},
  publisher = {Institute of Central Computation and Knowledge}
}

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