Version 1
: Received: 27 August 2024 / Approved: 27 August 2024 / Online: 28 August 2024 (00:14:06 CEST)
How to cite:
Zhao, R.; Zhu, W.; Han, C.; Wei, B.; Zhang, H.; Rahman, A.; Li, C. Pedestrian Fall Detection Methods for Public Traffic Areas: Literature Review. Preprints2024, 2024081968. https://doi.org/10.20944/preprints202408.1968.v1
Zhao, R.; Zhu, W.; Han, C.; Wei, B.; Zhang, H.; Rahman, A.; Li, C. Pedestrian Fall Detection Methods for Public Traffic Areas: Literature Review. Preprints 2024, 2024081968. https://doi.org/10.20944/preprints202408.1968.v1
Zhao, R.; Zhu, W.; Han, C.; Wei, B.; Zhang, H.; Rahman, A.; Li, C. Pedestrian Fall Detection Methods for Public Traffic Areas: Literature Review. Preprints2024, 2024081968. https://doi.org/10.20944/preprints202408.1968.v1
APA Style
Zhao, R., Zhu, W., Han, C., Wei, B., Zhang, H., Rahman, A., & Li, C. (2024). Pedestrian Fall Detection Methods for Public Traffic Areas: Literature Review. Preprints. https://doi.org/10.20944/preprints202408.1968.v1
Chicago/Turabian Style
Zhao, R., Arifur Rahman and Cuiling Li. 2024 "Pedestrian Fall Detection Methods for Public Traffic Areas: Literature Review" Preprints. https://doi.org/10.20944/preprints202408.1968.v1
Abstract
Crowd accident surveys have shown that regardless of the initial triggering factors, pedestrian fall behaviour is the most critical factor causing and aggravating crowd accidents in public traffic areas (PTAs). Application of pedestrian fall behaviour detection methods in PTAs is significant. Once deployed, it would prevent many pedestrians from losing life in crowed traffic areas accidents. However, most existing methods is still focused on medical assistance for the elderly. Therefore, this paper conducted bibliometric and content analyses, combining fall detection-related keywords to retrieve from internationally recognized literature databases and benchmark pedestrian behaviour datasets. Based on the analysis of the state-of-the-art (SOTA) achievements in fall detection methods, the fall detection methods were classified into different categories according to the research approach. Among them, it undertakes a comprehensive analysis of five predominant methods, namely computer vision, Internet of Things, smartphone, kinematic, and wearable device-based methods. Furthermore, the benchmark datasets including fall scenarios were introduced and compared. Finally, it provides a detailed discussion of existing fall detection methods, and possible future directions are identified considering application requirements in PTAs. This overview may help researchers understand the SOTA fall detection methods and devise new methodologies by improving and synthesizing the highlighted issues in PTAs.
Keywords
Fall detection; Fall detection dataset; Computer vision; Wearable device; Internet of Things; Kinematic; Public traffic area
Subject
Computer Science and Mathematics, Computer Vision and Graphics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
