## 4 Discussion  
In this study, we have proposed two evaluation methods to assess drinking activity recognition, one of which is the event-based method, which shows significantly better outcomes. This difference may stem from the fact that the sample-based approach is more susceptible to errors between predicted event start points and actual event start points. In the trial of Figure 14, all drinking activities are identified successfully, and an \(F1\)-score of 100% is achieved in this case with the event-based method. However, in the sample-based approach, a small number of misclassifications at event boundaries can lead to a certain extent of impact on the confusion matrix, resulting in a decrease in evaluation metrics. In comparison, the event-based method presents a more accurate performance evaluation in the recognition of drinking activities. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Modality** & **Recall (\%)** & **Precision (\%)** & **F1-Score (\%)** \\ \hline WCA & **93.2 \(\pm\) 5.1** & 76.2 \(\pm\) 5.4 & **83.7 \(\pm\) 3.8** \\ WC & 87.0 \(\pm\) 8.5 & 75.8 \(\pm\) 5.3 & 80.6 \(\pm\) 4.1 \\ WA & 90.9 \(\pm\) 7.5 & **76.6 \(\pm\) 5.3** & 82.9 \(\pm\) 4.6 \\ CA & 89.4 \(\pm\) 9.7 & 73.9 \(\pm\) 5.8 & 80.7 \(\pm\) 6.3 \\ W & 82.4 \(\pm\) 10.4 & 76.0 \(\pm\) 6.6 & 78.4 \(\pm\) 4.7 \\ C & 75.6 \(\pm\) 15.0 & 73.2 \(\pm\) 8.6 & 73.8 \(\pm\) 10.9 \\ A & 27.2 \(\pm\) 12.2 & 69.9 \(\pm\) 17.7 & 36.2 \(\pm\) 14.0 \\ \hline \hline \end{tabular} \(W\)—motion signal of both wrists; C—motion signal of the cup; A—acoustic signal; bold number—best performance in this column.  
\end{table} 
Table 7: Comparison table of the sample-based approach results for drinking activities recognition using support vector machine with different input signal combinations. Figure 14: Recognition results of drinking activities with multimodal signals. Based on the results of this study, when using multimodal signals for drinking activity recognition, the performance of single acoustic signals is greatly lower than that of single motion signals. This result may be attributed to the fact that swallowing is one of the most prominent features for identifying drinking activities in acoustic features. Nevertheless, there are certain differences in the swallowing features among different individuals. Additionally, other activities designed in this study, such as speaking and eating, increase the opportunities for participants to swallow. These non-drinking behaviors share similar features with swallowing during drinking in acoustic signals. Hence, when training classifiers utilizing single acoustic signals, these similar features may cause classifiers to confuse the two. In Figure 15, the ground truth and predicted drinking activity segments in a trial are marked with different colors. In the recognition results based on single acoustic signals, a segment mistakenly identified as a drinking activity is outlined in red. However, this segment actually corresponds to the swallowing in the latter part of eating, and the sole use of acoustic signals leads to misclassification. Since there is no action of raising the cup and pouring water into the mouse, the swallowing of the non-drinking behavior is not misclassified as drinking when utilizing multimodal signal recognition. Furthermore, in the processing of utilizing single acoustic signals for drinking activity recognition, this study observes that some drinking activities in the window sequence appear discontinuous. These window sequences are converted into sample sequences due to the fact that only one or two windows out of the four overlapping windows are labeled as drinking. These samples of drinking activities will be deleted during the voting process, thereby affecting the recognition results. As a consequence, these factors may explain why single acoustic signals perform poorly in drinking activity recognition.