# Pedestrian Fall Detection Methods for Public Traffic Areas: A Literature Review
###### Abstract  
Crowd accident surveys have shown that regardless of the initial triggering factors, pedestrian fall behavior is the most critical factor causing and aggravating crowd accidents in public traffic areas (PTAs). The application of pedestrian fall behavior detection methods in PTAs is significant. Once deployed, they would prevent many pedestrians from losing life in crowded traffic area accidents. However, most existing methods are still focused on medical assistance for the elderly. Therefore, this paper conducted bibliometric and content analyses, combining fall detection-related keywords from internationally recognized literature databases and benchmark pedestrian behavior 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. This study 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, this study provides a detailed discussion of existing fall detection methods, and possible future directions are identified considering the 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. full detection; public traffic area; literature review; computer vision; internet of things; smartphone; fall detection dataset +
Footnote †: journal: _sciences_  
_Applied_  
_Applied_  
Fall behavior is highly damaging owing to both physical and psychological injuries [3]. Accident surveys have shown that regardless of the initial triggering factors, pedestrian fall behavior is the most critical factor causing and aggravating crowd accidents [2]. However, in the past, most fall detection studies focused primarily on monitoring the health of the elderly and did not consider public traffic [3]. Detecting pedestrian falls in public places will not only allow timely notification to authorities for assistance but also effectively help to prevent subsequent crowd instability and stampede incidents, which is important. The most promising tools include wearable sensors, such as gyroscopes and accelerometers, and visual sensors, such as RGB, infrared, and depth cameras and camera arrays (for three-dimensional reconstruction). Wearable sensors capture data, such as the speed and angles of the wearer, and upon detecting abnormal values, they identify the occurrence of a fall and issue alerts to notify users and supervisors. However, this approach faces practical challenges, including device battery monitoring, frequent recharging, and inconvenience of wearing, which hinder its application and widespread adoption [4]. Fall detection based on visual sensors, computer vision (CV) and deep learning has become a prominent research direction integrating with other methods based on smartphones or the IoT. The increasing number of surveillance cameras in public places such as airports, train stations, subway stations, and roads can provide data support and the application prospects for fall detection using visual sensors. The major contributions of this paper are as follows:  
* To elaborate on existing and current techniques proposed for fall detection;
* To review related benchmark datasets for fall detection research;
* To provide a critical analysis by considering application requirements in PTAs and significant future guidelines with issues and solutions. The remainder of this paper is organized as follows. Section 2 describes the retrieval and analysis methodology of the fall-related literature, Section 3 elaborates on the literature of five prominent research methods, Section 4 is a detailed comparison of related benchmark datasets, and Section 5 discusses the critical challenges and future trends in PTAs.