## 5 Results  
The hyperparameter optimization results for the model trained on the whole dataset are provided in Appendix A. Despite even the worst-performing combination achieving an accuracy of over 90%, hyperparameter optimization was still necessary to discover the best combination of hyperparameters for improved performance on unseen test data. The best model exhibited a 98.9% training accuracy and 99% validation accuracy, outcomes achieved through hyperparameter optimization. The final model structure is given in Figure 4, and the hyperparameters are given in Table 3.  
The training and validation accuracies, as well as the loss versus epoch graphs, for the best-performing combination of hyperparameters are depicted in Figure 5. The optimized model achieved 98.3% accuracy and 0.02 loss on the training data and 94% accuracy and 0.018 loss on the validation data. These results indicate that the optimized model performed exceptionally well on the data on which it had been trained. However, the true performance would be assessed using the test data. Before delving into the results, it is essential to understand some key evaluation metrics used to assess the performance of classification models: precision, recall, and F1 score, as well as the confusion matrix. Figure 4: A layered view of the optimized model. Figure 5: The training and validation accuracy (**left**) and training and validation loss (**right**) for the optimized model. \begin{table}
\begin{tabular}{c c} \hline
**Hyperparameter** & **Optimal Value** \\ \hline n\_layers & 6 \\ conv\_0 & 288 \\ conv\_1 & 288 \\ conv\_2 & 224 \\ conv\_3 & 32 \\ dropout & 0.2 \\ dense layers & 3 \\ n\_nodes & 256 \\ \hline \end{tabular}
\end{table} 
Table 3: Optimum combination of hyperparameters obtained by random search method. Precision, also known as the positive predictive value, measures the accuracy of positive predictions made by the model. It is calculated as the ratio of true positive predictions to the total number of positive predictions made by the model. A high precision score indicates that the model has fewer false positive predictions. Recall, also known as the sensitivity or true positive rate, measures the ability of the model to correctly identify positive instances from the total actual positive instances in the dataset. It is calculated as the ratio of true positive predictions to the total number of actual positive instances. A high recall score indicates that the model can capture most of the positive instances. The F1 score is the harmonic mean of the precision and recall. It provides a balance between precision and recall, especially in cases where there is an imbalance between the number of positive and negative instances in the dataset. The F1 score is calculated as shown in Equation (1):  
\[2\times\frac{precision\times recall}{precision+recall} \tag{1}\]  
In order to gauge the performance of the optimized model, it was subjected to evaluation using the test data. The confusion matrix, depicted in Figure 6, and the corresponding classification report in Table 4 provide insights into the model's predictive capabilities. While the optimized model demonstrated high accuracy in predicting most damage cases, it exhibited some confusion, particularly with damage_7 classification. This poor performance for damage_7 might be attributed to various factors such as imbalanced data distribution, insufficient representation of damage_7 instances in the training dataset, or the presence of unique features in damage_7 cases that were not effectively captured by the model. Despite achieving F1 scores exceeding 90% for other damage categories, the F1 score for damage_7 stood at 86%, indicating relatively poorer performance in predicting instances of this specific damage type. However, it is worth noting that the overall accuracy for the test data remained commendable at 97%, surpassing acceptable levels. Furthermore, considering the potential influence of overfitting in the single-accelerometer models, addressing this issue through future work is imperative.