# Fall Detection Approaches for Monitoring Elderly HealthCare Using Kinect Technology: A Survey
## 4 Kinect-Based Fall Detection Approaches
### Interpretation in Control
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Paper** & **Data** & **Preprocessing** & **Features** & **Algorithm** & **Threshold** & **Performance** \\ \hline \multirow{6}{*}{[87]} & \multirow{6}{*}{- Depth} & \multirow{6}{*}{- Create a new coordinate system centered at the hip center - Calculate the 35 Skeletal data regarding the new coordinate system (1) Histogram based representation of the motion-pose - Calculate the displacement vectors and the signed distances for the skeletal joints - Estimate the pose profile characteristics} & \multirow{6}{*}{- SVM with a RBF kernel} & \multirow{6}{*}{-} & \multirow{6}{*}{- Mean Acc} \\  & & & & & & 78.77\% \\ \cline{1-1} \cline{5-5}  & & & & & & \\ \cline{1-1} \cline{5-5}  & & & & & & \\ \cline{1-1} \cline{5-5}  & & & & & & \\ \cline{1-1} \cline{5-5}  & & & & & & \\ \cline{1-1} \cline{5-5}  & & & & & & \\ \hline \multirow{6}{*}{[72]} & \multirow{6}{*}{- Skeletal} & \multirow{6}{*}{- Change axis in coordinates system - Apply a sliding windows of 2 s} & (1) Head velocity & & (1) 0.6 * (7/20 * * \\  & & & & & person’s height)/(2) \\ \cline{1-1} \cline{5-5}  & & & (2) Horizontal velocity of hip center & - Adaptive thresholding & (2) 0.55 * (person’s * \\  & & & & & & \\ \cline{1-1} \cline{5-5}  & & & & & & \\ \cline{1-1} \cline{5-5}  & & & & & & \\ \cline{1-1} \cline{5-5}  & & & & & & \\ \cline{1-1} \cline{5-5}  & & & & & & \\ \hline \multirow{6}{*}{[67]} & \multirow{6}{*}{- Depth} & \multirow{6}{*}{- Background subtraction - Noise filter - Calculate Depth histogram of the body detected} & (1) Depth range difference - Adaptive thresholding & & & - Acc 97.2\% \\ \cline{1-1} \cline{5-5}  & & & (2) Depth mean difference & - Adaptive thresholding & & - Error rate 2.8\% \\ \cline{1-1} \cline{5-5}  & & & (3) Inactivity & & & - Se 94\% \\ \cline{1-1} \cline{5-5}  & & & & & & - Sp 100\% \\ \hline \multirow{6}{*}{[54]} & \multirow{6}{*}{- Depth} & \multirow{6}{*}{- Median filter - Canny filter - Calculate blue the tangent vector angle of the outline binary image} & & & & - Acc 97.5\% \\ \cline{1-1} \cline{5-5}  & & & & & & - Error rate 2.9\% \\ \cline{1-1} \cline{5-5}  & & & & & & - Se 94.9\% \\ \hline \multirow{6}{*}{[42]} & \multirow{6}{*}{- Depth} & \multirow{6}{*}{- Image normalization - Image polar coordinates - FFT transform} & \multirow{6}{*}{(1) Wavelet moment} & \multirow{6}{*}{- Minimum distance - SVM with linear kernel} & \multirow{6}{*}{-} & \multirow{6}{*}{- Mean Acc 91\%} \\ \cline{1-1} \cline{5-5}  & & & & & & \\ \cline{1-1} \cline{5-5}  & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Paper** & **Data** & **Preprocessing** & **Features** & **Algorithm** & \begin{tabular}{l} **Threshold** \\ **Values** \\ \end{tabular}  & **Performance** \\ \hline \hline \multirow{8}{*}{**Paper**} & \multirow{8}{*}{-Depth} & \multirow{8}{*}{-Person extraction if sum vector of acceleration \(\times\)3 g} & \multirow{8}{*}{**(1) Height to width ratio of the person’s**} & \multirow{8}{*}{-Fuzzy logic} & \multirow{8}{*}{-Fuzzy logic} \\  & & & & & & \\  & & & & & & \\  & & & &