# Gesture Classification Using a Smartwatch: Focusing on Unseen Non-Target Gestures
## 3 Results
The results indicate that data augmentation positively influenced model performance, with varying degrees of improvement depending on the augmentation technique used. Without augmentation, the model achieved an average F1-score of 0.6604, with relatively poor performance on non-target patterns. This suggests that the model struggles to generalize effectively to unseen non-target movements in the absence of augmentation. Among the tested augmentation methods, Gaussian Noise led to the highest overall performance, with an average F1-score of 0.7323. This method significantly improved classification accuracy across all non-target patterns, particularly FRU-N (0.8411) and FLD-N (0.8016). The improved performance suggests that introducing noise enhances the model's robustness by preventing overfitting to specific movement characteristics. Time Shift augmentation also improved model performance, achieving an average F1-score of 0.6897. Notably, this method enhanced the classification of FRD-N (0.8130) and TRA-N (0.6439), suggesting that shifting temporal features allows the model to better capture movement variations. However, performance on FLU-N (0.5850) and FLD-N  
Figure 5: Confusion matrices of the proposed method with different unseen patterns: (**a**) Non-Target FRU, (**b**) Non-Target FRD, (**c**) Non-Target FLU, (**d**) Non-Target FLD, (**e**) Non-Target TRA. (0.6172) remained relatively low, indicating that some patterns may be more sensitive to temporal shifts. In contrast, Cut-out augmentation resulted in the lowest average F1-score (0.6236), with particularly poor performance on FLD-N (0.5444) and TRA-N (0.4673). The sharp decline in these scores suggests that randomly removing portions of the data may have \begin{table}
\begin{tabular}{c c c c c c c} \hline  & **FRU-N** & **FRD-N** & **FLU-N** & **FLD-N** & **TRA-N** & **Mean \(\pm\) Std** \\ \hline No augmentation & 0.7732 \(\pm\) 0.0668 & 0.6292 \(\pm\) 0.1211 & 0.6878 \(\pm\) 0.0676 & 0.7103 \(\pm\) 0.0916 & 0.5015 \(\pm\) 0.0795 & 0.6604 \(\pm\) 0.1252 \\ \hline Time Shift & 0.7893 \(\pm\) 0.0728 & 0.813 \(\pm\) 0.0615 & 0.5850 \(\pm\) 0.1115 & 0.6172 \(\pm\) 0.1403 & 0.6439 \(\pm\) 0.1036 & 0.6897 \(\pm\) 0.1357 \\ \hline Gaussian Noise & 0.8411 \(\pm\) 0.0698 & 0.7663 \(\pm\) 0.1032 & 0.7385 \(\pm\) 0.1115 & 0.8016 \(\pm\) 0.0525 & 0.5142 \(\pm\) 0.1024 & 0.7323 \(\pm\) 0.1446 \\ \hline Cut-out & 0.7255 \(\pm\) 0.0988 & 0.6955 \(\pm\) 0.0648 & 0.6853 \(\pm\) 0.0988 & 0.5444 \(\pm\) 0.2082 & 0.4673 \(\pm\) 0.1221 & 0.6236 \(\pm\) 0.1590 \\ \hline \end{tabular}
\end{table} 
Table 4: Model performance on unseen non-target patterns with different augmentation methods. Figure 6: Loss and accuracy curves for training and validation on different unseen patterns: (**a**) Non-Target FLD, (**b**) Non-Target FLU, (**c**) Non-Target FRD, (**d**) Non-Target FRU, (**e**) Non-Target TRA. disrupted key motion features, making it more difficult for the model to learn meaningful representations. A two-way repeated measures ANOVA was conducted to examine the effects of augmentation methods and non-seen patterns on performance, using SPSS Statistics (version 29). A significant main effect of augmentation method was observed, \(F_{(3,27)}=16.295\), \(p<0.001\), with a large effect size (\(\eta^{2}=0.644\)), indicating that different augmentation methods resulted in significant differences in performance. Post-hoc comparisons using Bonferroni correction revealed that Gaussian Noise significantly outperformed the condition without augmentation (\(p<0.01\)) and Cut-out (\(p<0.001\)), while Time Shift also demonstrated a significantly higher F1-score than Cut-out (\(p<0.01\)). These findings suggest that augmentation techniques, particularly Gaussian Noise, play a crucial role in improving model performance, whereas Cut-out resulted in the lowest accuracy. These findings highlight the importance of selecting an appropriate augmentation method to improve model robustness.