# Pedestrian Fall Detection Methods for Public Traffic Areas: A Literature Review
## 3 SOTA Methods for Fall Detection
### CV-Based Methods
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Year** & **Authors** & **Method** & **Scenario** & **Main Contribution** \\ \hline
2019 & [PERSON] et al. [21] & K & \
\begin{tabular}{c} Medical \\ Assistance \\ \end{tabular} &
\begin{tabular}{c} Analysed the effects of fall history on \\ kinematic synergy \\ \end{tabular} \\ \hline
2020 & [PERSON] et al. [22] & K & \
\begin{tabular}{c} Medical \\ Assistance \\ \end{tabular} &
\begin{tabular}{c} Proposed an approach for the reorganization of \\ accidental falls using OpenPose to extract three key \\ parameters of skeletal information from the \\ human body \\ \end{tabular} \\ \hline
2020 & [PERSON] et al. [23] & CV & Public Traffic &
\begin{tabular}{c} Proposed an attention-guided LSTM model for fall \\ detection in complex scenes \\ \end{tabular} \\ \hline
2021 & [PERSON] et al. [24] & SP & \
\begin{tabular}{c} Medical \\ Assistance \\ \end{tabular} &
\begin{tabular}{c} Proposed a smartphone application that included \\ assessment, management, and prevention of fall risk \\ \end{tabular} \\ \hline
2021 & [PERSON] et al. [25] & K & \
\begin{tabular}{c} Medical \\ Assistance \\ \end{tabular} &
\begin{tabular}{c} Studied CoM characteristics of elderly with fall \\ history using the UCM to collect and analyze \\ kinematic data \\ \end{tabular} \\ \hline
2021 & [PERSON] et al. [26] & IoT & \
\begin{tabular}{c} Medical \\ Assistance \\ \end{tabular} &
\begin{tabular}{c} Proposed an AI-based CNN for fall behavior analysis; \\ the IoT framework was constructed using a \\ Hadoop-distributed file system module \\ \end{tabular} \\ \hline
2022 & [PERSON] et al. [27] & WD & \
\begin{tabular}{c} Medical \\ Assistance \\ \end{tabular} &
\begin{tabular}{c} Compared statistical characteristics of acceleration \\ signals and incorporated a set of small sensing modes \\ in the system \\ \end{tabular} \\ \hline
2022 & [PERSON] et al. [28] & WD & \
\begin{tabular}{c} Medical \\ Assistance \\ \end{tabular} &
\begin{tabular}{c} Proposed a novel variation of the deep learning \\ model that integrated a hierarchical attention \\ mechanism into a CNN \\ \end{tabular} \\ \hline
2022 & [PERSON] et al. [29] & CV & Public Traffic &
\begin{tabular}{c} Proposed a real-time abnormal behavior detection \\ model based on LSTM \\ \end{tabular} \\ \hline
2022 & [PERSON] et al. [30] & CV & Public Traffic &
\begin{tabular}{c} Proposed a fall prediction system based on TOLOv3 \\ and attention weight factor \\ \end{tabular} \\ \hline
2022 & [PERSON] et al. [31] & WD & Public Traffic &
\begin{tabular}{c} Proposed a new fall testing method using ATD \\ anthropomorphic manikin and three-axis \\ acceleration transducers \\ \end{tabular} \\ \hline
2022 & [PERSON] et al. [32] & CV & Public Traffic &
\begin{tabular}{c} Proposed a lightweight fall detection algorithm using \\ GhostNet to replace the DSPDarknet53 \\ \end{tabular} \\ \hline
2023 & [PERSON] et al. [33] & IoT & \
\begin{tabular}{c} Medical \\ Assistance \\ \end{tabular} &
\begin{tabular}{c} Proposed an energy-aware IoT-based architecture \\ and a hybrid double-prediction technique \\ \end{tabular} \\ \hline
2023 & [PERSON] et al. [34] & WD & \
\begin{tabular}{c} Medical \\ Assistance \\ \end{tabular} &
\begin{tabular}{c} Used TinyCNN with two-stage efficient \\ feature extraction \\ \end{tabular} \\ \hline
2023 & [PERSON] et al. [35] & CV & \
\begin{tabular}{c} Medical \\ Assistance \\ \end{tabular} &
\begin{tabular}{c} The proposed pre-posed attention capture \\ mechanism combines human pose-based and key \\ point-based models \\ \end{tabular} \\ \hline \hline \end{tabular}  
Note: CV: computer vision, IoT: Internet of Things, WD: wearable device, K: kinematic, SP: smartphone.  
\end{table} 
Table 1: _Cont._the occurrence of a fall by studying the velocity of the deformation experienced by the segmented region of interest along an established time interval. However, the validation phase was limited to a few experiments using self-acquired videos, which cast doubt on the reliability of the experimental results. [PERSON] et al.