# Hybrid Representation of Sensor Data for the Classification of Driving Behaviour
## 4 Experimental Evaluation
### Results
#### 4.3.2 Route Level Classification  
In this section, we evaluate the classification performance of the three variants of the proposed approach (CNN, LSTM and GRU) at the route level. To provide further insights, we also investigate the performance of standalone NN-based and rule-based components. Finally, we perform quantitative and qualitative comparisons with state-of-the-art. Table 6 presents the confusion matrices for the classification at the route level, as performed by the three variants of the proposed approach (CNN, LSTM and GRU) on each one of the three datasets described in Section 4.1 (UAH, MOTIF 1 and MOTIF 2). Hybrid variants, which employ rule-based event detection, are presented without parentheses, whereas their corresponding NN-only variants are presented in parentheses. It could be observed that:  
1. In almost all cases, each hybrid variant obtains equal or higher classification accuracy, when compared to its NN-only counterpart (one exception arises in CNN-based classification of normal samples in the MOTIF 1 dataset). This indicates that the rule-based component contributes to overall classification performance and in several cases 'corrects' the result obtained by the NN-based component. 2. When comparing the three NN architectures investigated, the RNN-based ones (LSTM and GRU) obtain a higher classification performance than the CNN-based architecture, more so in the UAH and MOTIF 2 datasets. This could be attributed to the fact \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{UAH} \\ \hline  & Acc & P & R & F1 \\ CNN & 0.64 & 0.63 & 0.64 & 0.60 \\ LSTM & 0.89 & 0.89 & 0.89 & 0.89 \\ GRU & 0.91 & 0.91 & 0.91 & 0.91 \\ \hline  & \multicolumn{4}{c}{MOTIF 1} \\ \hline  & Acc & P & R & F1 \\ CNN & 0.82 & 0.83 & 0.82 & 0.82 \\ LSTM & 0.99 & 0.99 & 0.99 & 0.99 \\ GRU & 0.99 & 1.00 & 1.00 & 1.00 \\ \hline  & \multicolumn{4}{c}{MOTIF 2} \\ \hline  & Acc & P & R & F1 \\ CNN & 0.78 & 0.79 & 0.78 & 0.78 \\ LSTM & 0.97 & 0.97 & 0.97 & 0.97 \\ GRU & 0.96 & 0.97 & 0.97 & 0.97 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Accuracy, Precision, Recall and F1 measures for time slice classification. that RNNs have been formulated to capture patterns in time-series such as these sensor measurements. Between LSTM and GRU, the latter achieves slightly more accurate classification. To facilitate comparisons, Table 7 provides a more detailed view of the classification results in the UAH dataset, including the results obtained by the approach of [PERSON] et al. [12]. Each classification result is marked as 'T' (True) or 'F' (False). We included the results obtained by the standalone rule-based component ('Events (only)') in order to highlight its contribution to overall classification accuracy obtained by the hybrid variants. It could be observed that:  
1. The GRU-based hybrid variant ('Hybrid (GRU)') has 1/23 misclassification, whereas the LSTM-based and CNN-based hybrid variants ('Hybrid (CNN)' and 'Hybrid (LSTM)') have 6/23 and 2/23 misclassifications. The approach of [PERSON] et al. [12] has 3/23 misclassifications. 2. All 'NN-only' variants lead to a considerable number of misclassifications. Still, when 'NN-only' variants are combined with 'Events-only', the overall classification accuracy