# Pedestrian Fall Detection Methods for Public Traffic Areas: A Literature Review
## 3 SOTA Methods for Fall Detection
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Year** & **Authors** & **Method** & **Scenario** & **Main Contribution** \\ \hline
2013 & [PERSON] et al. [6] & CV & \
\begin{tabular}{c} Medical \\ Assistance \\ \end{tabular} &
\begin{tabular}{c} Proposed a fuzzy model to increase flexibility, \\ avoiding limitations in the representation of human \\ figures and optimizing the evaluation parameters \\ \end{tabular} \\ \hline
2014 & [PERSON] et al. [7] & CV & \
\begin{tabular}{c} Medical \\ Assistance \\ \end{tabular} &
\begin{tabular}{c} Proposed a partially occluded fall detection method \\ using vertical velocity as an assistive feature to avoid \\ interfering actions \\ \end{tabular} \\ \hline
2015 & [PERSON] et al. [8] & K & \
\begin{tabular}{c} Medical \\ Assistance \\ \end{tabular} &
\begin{tabular}{c} Proposed a novel fall detection model based on a \\ statistical process control chart using a linear \\ combination to define the fall indicator \\ \end{tabular} \\ \hline
2015 & [PERSON] et al. [9] & SP & \
\begin{tabular}{c} Medical \\ Assistance \\ \end{tabular} &
\begin{tabular}{c} Determined the sensitivity and specificity of \\ smartphone-based fall detection and the placement \\ of the smartphone \\ \end{tabular} \\ \hline
2016 & [PERSON] et al. [10] & IoT & \
\begin{tabular}{c} Medical \\ Assistance \\ \end{tabular} &
\begin{tabular}{c} Proposed an IoT-based wearable fall detection \\ system that minimized the energy consumption of \\ the wearable sensor node \\ \end{tabular} \\ \hline
2016 & [PERSON] et al. [11] & SP & \
\begin{tabular}{c} Medical \\ Assistance \\ \end{tabular} &
\begin{tabular}{c} Proposed a smartphone-based fall detection system \\ that incorporates sensing motes \\ \end{tabular} \\ \hline
2017 & [PERSON] et al. [12] & IoT & \
\begin{tabular}{c} Medical \\ Assistance \\ \end{tabular} &
\begin{tabular}{c} Proposed an IoT-based multi-user perspective \\ information system using a three-axial accelerometer, \\ pedestrian dead reckoning, and a decision \\ tree algorithm \\ \end{tabular} \\ \hline
2017 & [PERSON] et al. [13] & SP & Public Traffic &
\begin{tabular}{c} The proposed system measures the ground distance \\ from the smartphone and the infrared sensor \\ \end{tabular} \\ \hline
2017 & [PERSON] et al. [14] & K & \
\begin{tabular}{c} Medical \\ Assistance \\ \end{tabular} &
\begin{tabular}{c} Proposed a generic model for estimating hip-impact \\ forces using a generic model to estimate \\ hip-impact forces \\ \end{tabular} \\ \hline
2017 & [PERSON] et al. [15] & SP & \
\begin{tabular}{c} Medical \\ Assistance \\ \end{tabular} &
\begin{tabular}{c} Proposed a fall detection algorithm based on a \\ threshold using an SVM to classify activities of \\ daily living \\ \end{tabular} \\ \hline
2018 & [PERSON] et al. [3] & IoT & \
\begin{tabular}{c} Medical \\ Assistance \\ \end{tabular} &
\begin{tabular}{c} Combined real-time and future fall prediction and \\ prevention algorithms using the computational \\ capabilities of IoT nodes \\ \end{tabular} \\ \hline
2018 & [PERSON] et al. [16] & SP & \
\begin{tabular}{c} Medical \\ Assistance \\ \end{tabular} &
\begin{tabular}{c} Showed the smartphone as a valid measurement tool \\ for postural stability \\ \end{tabular} \\ \hline
2019 & [PERSON] et al. [17] & CV & \
\begin{tabular}{c} Medical \\ Assistance \\ \end{tabular} &
\begin{tabular}{c} Fall inclination prediction based on a depth camera \\ using principal component analysis to calculate the 3D inclination \\ \end{tabular} \\ \hline
2019 & [PERSON] et al. [18] & IoT & \
\begin{tabular}{c} Medical \\ Assistance \\ \end{tabular} &
\begin{tabular}{c} Defined two types of falls and analyzed major \\ fall parameters \\ \end{tabular} \\ \hline
2019 & [PERSON] et al. [19] & WD & \
\begin{tabular}{c} Medical \\ Assistance \\ \end{tabular} &
\begin{tabular}{c} Proposed a wearable sensor-based continuous fall \\ monitoring system that was further investigated \\ through three machine learning algorithms \\ \end{tabular} \\ \hline
2019 & [PERSON] et al. [20] & WD & \
\begin{tabular}{c} Medical \\ Assistance \\ \end{tabular} &
\begin{tabular}{c} Proposed a novel fall detection system using \\ wearable sensors that exploited the covariance matrix \\ as a feature extractor and fusion approach \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}