## 3 Results
### Drinking Activity Identification Using Multimodal Signal
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classifier** & **Modality** & **Recall (\%)** & **Precision (\%)** & **F1-Score (\%)** \\ \hline \multirow{4}{*}{SVM} & M & 97.2 \(\pm\) 4.6 & 91.0 \(\pm\) 8.5 & 93.9 \(\pm\) 6.0 \\  & A & 45.6 \(\pm\) 16.8 & 79.5 \(\pm\) 18.5 & 54.5 \(\pm\) 15.2 \\  & MA & **98.1 \(\pm\) 3.5** & **95.0 \(\pm\) 7.7** & **96.5 \(\pm\) 5.5** \\ \hline \multirow{4}{*}{\(k\)-NN} & M & 98.1 \(\pm\) 4.5 & 90.3 \(\pm\) 9.7 & 93.9 \(\pm\) 7.4 \\  & A & 35.0 \(\pm\) 16.1 & 65.1 \(\pm\) 28.4 & 46.1 \(\pm\) 15.6 \\  & MA & 97.2 \(\pm\) 5.0 & 92.6 \(\pm\) 10.5 & 94.7 \(\pm\) 7.8 \\ \hline \multirow{4}{*}{RF} & M & 96.6 \(\pm\) 8.7 & 94.2 \(\pm\) 12.8 & 95.2 \(\pm\) 10.9 \\  & A & 29.7 \(\pm\) 14.9 & 85.3 \(\pm\) 21.2 & 44.2 \(\pm\) 16.0 \\ \cline{1-1}  & MA & 97.2 \(\pm\) 7.3 & 94.6 \(\pm\) 10.9 & 95.7 \(\pm\) 9.0 \\ \hline \multirow{4}{*}{XGBoost} & M & 96.6 \(\pm\) 5.8 & 90.6 \(\pm\) 10.5 & 93.4 \(\pm\) 8.2 \\ \cline{1-1}  & A & 45.6 \(\pm\) 18.7 & 78.1 \(\pm\) 20.7 & 53.8 \(\pm\) 17.2 \\ \cline{1-1}  & MA & 96.9 \(\pm\) 5.0 & 93.6 \(\pm\) 10.5 & 95.1 \(\pm\) 7.9 \\ \hline \hline \end{tabular}  
* M—motion signal from the cup and both wrists; A—acoustic signal; MA—both motion and acoustic signals; bold number—best performance in this column.  
\end{table} 
Table 6: Comparison of the event-based method results for drinking activity recognition utilizing different classifiers on multimodal signals and single-modal signals. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classifier** & **Modality** & **Recall (\%)** & **Precision (\%)** & **F1-Score (\%)** \\ \hline \multirow{4}{*}{SVM} & M & 87.0 \(\pm\) 8.5 & 75.8 \(\pm\) 5.3 & 80.6 \(\pm\) 4.1 \\  & A & 27.2 \(\pm\) 12.2 & 69.9 \(\pm\) 17.7 & 36.2 \(\pm\) 14.0 \\  & MA & **93.2 \(\pm\) 5.1** & 76.2 \(\pm\) 5.4 & 83.7 \(\pm\) 3.8 \\ \hline \multirow{4}{*}{\(k\)-NN} & M & 86.4 \(\pm\) 6.6 & 73.7 \(\pm\) 6.0 & 79.3 \(\pm\) 4.7 \\  & A & 18.7 \(\pm\) 9.9 & 58.9 \(\pm\) 22.8 & 27.4 \(\pm\) 13.2 \\  & MA & 88.5 \(\pm\) 5.8 & 74.2 \(\pm\) 5.3 & 80.5 \(\pm\) 3.9 \\ \hline \multirow{4}{*}{RF} & M & 81.1 \(\pm\) 10.6 & 77.3 \(\pm\) 6.4 & 78.6 \(\pm\) 6.2 \\  & A & 15.4 \(\pm\) 8.7 & 73.2 \(\pm\) 22.1 & 24.7 \(\pm\) 12.6 \\ \cline{1-1}  & MA & 84.2 \(\pm\) 11.5 & 77.3 \(\pm\) 6.1 & 80.2 \(\pm\) 7.5 \\ \hline \multirow{4}{*}{XGBoost} & M & 87.5 \(\pm\) 8.7 & 76.5 \(\pm\) 5.7 & 81.2 \(\pm\) 4.7 \\ \cline{1-1}  & A & 28.1 \(\pm\) 13.8 & 70.8 \(\pm\) 17.1 & 37.7 \(\pm\) 15.6 \\ \cline{1-1}  & MA & 92.1 \(\pm\) 5.4 & **77.3 \(\pm\) 5.0** & **83.9 \(\pm\) 3.4** \\ \hline \hline \end{tabular}  
* M—motion signal from the cup and both wrists; A—acoustic signal; MA—both motion and acoustic signals; bold number—best performance in this column.  
\end{table} 
Table 5: Comparison of the sample-based approach results for drinking activity recognition utilizing different classifiers on multimodal signals and single-modal signals. performance of multimodal signals (MA). The results of the sample-based approach are shown in Table 7. WCA obtains the highest \(F1\)-score of 83.7% and the lowest standard deviation. WA achieves the second-highest \(F1\)-score of 82.9%. The results of the event-based method are shown in Table 8, which also demonstrates WCA performs the best, and WA comes second.