# A Framework for Fall Detection Based on OpenPose Skeleton and LSTM/GRU Models
## 3 Model Evaluation and Results
### RNN, LSTM and GRU Model Evaluation Indicators  
This section discusses the evaluation indicators of the confusion matrix of the three models of each normalization method to evaluate the effectiveness of the model. The structure and parameters of the model used in the above subsections are used to obtain  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Models** & **LR = 0.1** & **LR = 0.01** & **LR = 0.001** \\ \hline
**RNN** & 87\% & 87.3\% & 87\% \\
**LSTM** & 97\% & 98.3\% & 96.6\% \\
**GRU** & 87\% & 97\% & 95\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of average accuracy of model learning rate (RP).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Models** & **LR = 0.1** & **LR = 0.01** & **LR = 0.001** \\ \hline
**RNN** & 88.63\% & 87\% & 83\% \\
**LSTM** & 93.6\% & 93.5\% & 93\% \\
**GRU** & 96.3\% & 95\% & 92\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of average accuracy of model learning rate (RP + Linear Interpolation).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Models** & **Node = 64** & **Node = 128** & **Node = 256** & **Node = 512** & **Node = 1024** \\ \hline
**RNN** & 82.6\% & 84\% & 86.3\% & 87.3\% & **88\%** \\
**LSTM** & 96\% & 96\% & 97.3\% & 98.3\% & 94.6\% \\
**GRU** & 93\% & 94\% & 96\% & 96.6\% & 94.6\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Model accuracy vs. number of nodes.  
higher accuracy structures and parameters. The unnormalized, min-max normalization, and relative positions use 512 hidden Nodes and a learning rate of 0.01. Here, in linear interpolation we used the learning rate of 0.1.  
Table 8 shows the sensitivity, specificity and accuracy of RNN for the no-normalization, min-max normalization, RP-normalization and linear interpolation+RP-normalization methods, respectively. Here, a higher sensitivity indicates a more accurate estimation of fall events. Accuracy indicates that the model correctly classifies fall and non-fall events, and the higher the accuracy, the greater the number of correctly classified events. This shows that the overall accuracy of the RNN model is up to 90.1%, using relative position plus linear interpolation. Although the specificity is increased, the sensitivity is reduced, and the highest sensitivity is the relative position normalization method.  
Table 9 shows the above indicators for LSTM. It shows that the overall effect of using the maximum and minimum values is lower than that of unnormalized. The relative position method shows higher sensitivity and specificity, and lower error, than other methods when linear interpolation compensation is used. The effect of applying linear interpolation does not seem to be able to significantly improve the accuracy.  
Table 10 shows the above indicators for GRU. The GRU model has looser feature selection conditions due to reduced structure. Therefore, in the unnormalized dataset, it does not have the same accuracy as LSTM, but after using relative position normalization, the specificity is obvious increased from 76.7% to 98.2%, representing the classification accuracy of non-fall events. The overall accuracy has increased from 85.2% to 97.3%. Although the accuracy of GRU is not as high as LSTM, the specificity is higher than that of the other three models.