# A Framework for Fall Detection Based on OpenPose Skeleton and LSTM/GRU Models
## 4 Discussion  
This paper uses linear interpolation to compensate for the missing points. The experimental results show that the accuracy has not increased significantly. As shown in Figure 18, the missing point compensation is not as smooth as expected. Because there are nodes that change direction, we call it the turning point. For example, lifting the hand up is an  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Evaluation** & **No-** & **Min-Max** & **RP- Normal-** & **Linear Interpolation +** \\
**Indicators** & **Normalization** & **Normalization** & **RP-Normalization** \\ \hline
**Sensitivity** & 93.7\% & 93.7\% & 96.4\% & 96.4\% \\
**Specificity** & 76.7\% & 75.8\% & 98.2\% & 98.2\% \\
**Accuracy** & 85.2\% & 84.8\% & 97.3\% & 97.3\% \\ \hline \hline \end{tabular}
\end{table}
Table 10: Comparison of evaluation indicators of GRU model normalization methods.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Evaluation** & **No-** & **Min-Max** & **RP- Normal-** & **Linear Interpolation +** \\
**Indicators** & **Normalization** & **Normalization** & **RP-Normalization** \\ \hline
**Sensitivity** & 86.6\% & 73.2\% & 93.7\% & 92.8\% \\
**Specificity** & 83.9\% & 78.5\% & 84.8\% & 87.5\% \\
**Accuracy** & 85.2\% & 75.8\% & 89.2\% & 90.1\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison of evaluation indicators of RNN model normalization methods.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Evaluation** & **No-** & **Min-Max** & **RP- Normal-** & **Linear Interpolation +** \\
**Indicators** & **Normalization** & **Normalization** & **RP-Normalization** \\ \hline
**Sensitivity** & 96\% & 94\% & 100\% & 95.5\% \\
**Specificity** & 89.2\% & 83.9\% & 96.4\% & 94.6\% \\
**Accuracy** & 92.4\% & 88.8\% & 98.2\% & 95\% \\ \hline \hline \end{tabular}
\end{table}
Table 9: Comparison of evaluation indicators of LSTM model normalization methods.  
inertial motion, so its direction can be predicted, but it is difficult to estimate when you will reach the highest point or how long you will stay at the highest point. It is shown that the part circled in red in the figure is the turning point of the action, from which the point starts to change direction. Figure 18a shows that the turning point is missing, but the \(y\) points of the sequence 14 and 16 are not lost. The distance is relatively close, so the error is less than or equal to 1. In Figure 18b, the \(y\) points of the sequence 10 to 20 are missing, including the turning point. Using the 9 and 21 points of the sequence to calculate the compensation value, we can see that the error is quite large, because the exact position of the turning point is not known and the interpolation calculation encounters a large error, where the maximum error is 5. In the case of a lost turning point, the maximum error is the lost turning point minus the compensation turning point.  
Because the turning point cannot be estimated correctly. It causes wrong compensation when compensating for missing values, leading to features being eliminated or complicated. The inability of the model to learn effectively is the reason the accuracy cannot be further improved.