## 3 Results
### Fall Risk Assessment System
From this, an ablation test is applied to determine which activation function presents a better performance, these are presented in Table 4.  
For the final model, ReLU was chosen, which showed a performance superior to the rest, in addition to a consistent performance when using cross-validation. Figure 7 shows a diagram of the algorithm structure with ReLU as the hidden layer activation function and softmax as the output layer. The training and validation of the ANN were carried out with 500 iterations, obtaining the precision and loss graphs shown in Figure 8, where both settle around iteration 300 with an error in the steady state. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Parameters**} & \multicolumn{4}{c}{**Performance**} \\ \cline{2-10}  & \begin{tabular}{c} **No. of** \\ **Inputs** \\ \end{tabular} & \begin{tabular}{c} **Hidden Layers** \\ **Activation** \\ **Function** \\ \end{tabular} & \begin{tabular}{c} **No. of** \\ **Outputs** \\ \end{tabular} & \begin{tabular}{c} **Accuracy** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **Precision** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **Recall** \\ **(\%)** \\ \end{tabular} & \
\begin{tabular}{c} **F1-Score** \\ **(\%)** \\ \end{tabular} &
\begin{tabular}{c} **Specificity** \\ **(\%)** \\ \end{tabular} \\ \hline Test 1 & 18 & Sigmoid & 11 & 85.1 & 42.7 & 58.1 & 45.6 & 90.6 \\ \hline Test 2 & 18 & ReLU & 11 & 99.1 & 94.4 & 96.9 & 96.5 & 99.4 \\ \hline Test 3 & 18 & Tanh & 11 & 98.4 & 93.3 & 93.9 & 93.4 & 99.0 \\ \hline Test 4 & 18 & Selu & 11 & 98.6 & 94.8 & 96.1 & 95.5 & 99.2 \\ \hline Test 5 & 18 & Linear & 11 & 89.2 & 57.1 & 65.5 & 55.2 & 93.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Ablation test for the activation function selection. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Parameters**} & \multicolumn{4}{c}{**Performance**} \\ \cline{2-10}  & \begin{tabular}{c} **No. of** \\ **Inputs** \\ \end{tabular}  & \begin{tabular}{c} **First** \\ **No. of** \\ **Perceptrons** \\ \end{tabular}  & \begin{tabular}{c} **Second** \\ **No. of** \\ **Perceptrons** \\ \end{tabular}  & \begin{tabular}{c} **Third** \\ **No. of** \\ **Perceptrons** \\ \end{tabular}  & \begin{tabular}{c} **Fourth** \\ **Outputs** \\ \end{tabular}  & \begin{tabular}{c} **Accuracy** \\ **(\%)** \\ \end{tabular}  & \begin{tabular}{c} **Precision** \\ **(\%)** \\ \end{tabular}  & \begin{tabular}{c} **Recall** \\ **(\%)** \\ \end{tabular}  & \ \begin{tabular}{c} **F1-Score** \\ **(\%)** \\ \end{tabular}  & \begin{tabular}{c} **Specificity** \\ **(\%)** \\ \end{tabular}  \\ \hline Test 1 & 18 & 50 & - & - & - & 11 & 97.3 & 79.8 & 90.6 & 83.8 & 98.4 \\ \hline Test 2 & 18 & 50 & 32 & - & - & 11 & 98.5 & 90.2 & 92.9 & 90.3 & 99.0 \\ \hline Test 3 & 18 & 50 & 32 & 21 & - & 11 & 98.5 & 94.8 & 92.1 & 93.0 & 99.1 The final ANN model was cross-validated, resulting in a confusion matrix shown in Figure 9. From this, the performance is determined, obtaining a precision of 94.4%, a recall of 96.9%, an accuracy of 99.1%, a specificity of 99.4%, and an F1-Score of 95.5%, all of which were the average obtained between the different classes. Given the promising results of the trained network, it was decided to use the same structure to train a network that could classify each case as at risk or not at risk. For this, the same input information is used, but the desired output is labeled as 0 for fall risk cases and 1 for no risk cases. These cases are defined based on what was established in Section 2.3, where a score from 19 to 23 is at risk of falling, while a score above 24 is not at risk of falling. Figure 10 shows the result of the network trained by implementing cross-validation, obtaining an accuracy of 99.1%, a precision of 95.6%, a recall of 99.5%, an F1-Score of 97.4%, and a specificity of 99.5%. Figure 8: Graphs obtained from the training and validation of the ANN. (**A**). Loss. (**B**). Accuracy. Figure 10: Validation of fall risk classification using a confusion matrix.