# Gesture Classification Using a Smartwatch: Focusing on Unseen Non-Target Gestures
## 4 Discussion
This improvement can be attributed to the model's architecture and the loss function designed specifically to handle such cases. On the other hand, the proposed model showed the lowest performance when classifying TRA among all models, indicating a limitation that warrants further research to improve model robustness in this aspect. To further enhance classification performance, future improvements may involve integrating additional feature representations or refining training strategies. One potential approach could be increasing the diversity of non-target samples during training to improve the model's ability to generalize across unseen movements. Additionally, incorporating temporal dynamics more effectively through advanced sequence modeling techniques may help mitigate the observed misclassification trends. Non-target data used in this study were intentionally designed to closely resemble target gestures while differing in subtle ways. This makes their classification inherently more difficult than distinguishing gestures that are entirely dissimilar. While gestures that are obviously distinct from the target set can often be detected using methods such as anomaly detection, our focus is on the more challenging case where structural similarities exist. Previous work, such as [29], has examined OOD classification with EMG-based \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **Ref.** & **Year** & **FLD-N** & **FLU-N** & **FRD-N** & **FRU-N** & **TRA-N** & **Mean \(\pm\) Std** \\ \hline VGG16 (1D) & [37] & 2015 & 0.6318 \(\pm\) 0.1314 & 0.7461 \(\pm\) 0.089 & 0.5704 \(\pm\) 0.1278 & 0.6875 \(\pm\) 0.0704 & 0.7222 \(\pm\) 0.1148 & 0.6716 \(\pm\) 0.1228 \\ \hline VGG19 (1D) & [37] & 2015 & 0.698 \(\pm\) 0.1495 & 0.6675 \(\pm\) 0.1204 & 0.5703 \(\pm\) 0.0835 & 0.6375 \(\pm\) 0.1041 & 0.8231 \(\pm\) 0.0508 & 0.6793 \(\pm\) 0.1327 \\ \hline Inception (1D) & [38] & 2016 & 0.6072 \(\pm\) 0.095 & 0.659 \(\pm\) 0.0983 & 0.7229 \(\pm\) 0.0607 & 0.6845 \(\pm\) 0.0949 & 0.7946 \(\pm\) 0.0502 & 0.6936 \(\pm\) 0.1013 \\ \hline Xception (1D) & [39] & 2017 & 0.5792 \(\pm\) 0.2001 & 0.6295 \(\pm\) 0.1856 & 0.6824 \(\pm\) 0.1281 & 0.7810 \(\pm\) 0.0999 & 0.7756 \(\pm\) 0.1219 & 0.6895 \(\pm\) 0.1664 \\ \hline NasNet (1D) & [40] & 2018 & 0.5625 \(\pm\) 0.179 & 0.6619 \(\pm\) 0.1109 & 0.6961 \(\pm\) 0.1034 & 0.622 \(\pm\) 0.1516 & 0.6832 \(\pm\) 0.0538 & 0.6452 \(\pm\) 0.1296 \\ \hline EfficientNet (1D) & [41] & 2019 & 0.6211 \(\pm\) 0.1733 & 0.5048 \(\pm\) 0.1062 & 0.6093 \(\pm\) 0.1656 & 0.6656 \(\pm\) 0.1234 & 0.7706 \(\pm\) 0.1169 & 0.6343 \(\pm\) 0.1597 \\ \hline ConvNext (1D) & [42] & 2022 & 0.7019 \(\pm\) 0.1119 & 0.5999 \(\pm\) 0.137 & 0.557 \(\pm\) 0.1186 & 0.6802 \(\pm\) 0.0881 & 0.7601 \(\pm\) 0.0699 & 0.6598 \(\pm\) 0.1267 \\ \hline TimeMixer & [43] & 2024 & 0.5678 \(\pm\) 0.1154 & 0.5605 \(\pm\) 0.1034 & 0.6011 \(\pm\) 0.0729 & 0.6330 \(\pm\) 0.1246 & 0.4042 \(\pm\) 0.0813 & 0.5533 \(\pm\) 0.1257 \\ \hline MordenTCN & [44] & 2024 & 0.6583 \(\pm\) 0.2536 & 0.7547 \(\pm\) 0.0916 & 0.5970 \(\pm\) 0.2104 & 0.7736 \(\pm\) 0.1116 & 0.8272 \(\pm\) 0.1174 & 0.7222 \(\pm\) 0.1826 \\ \hline RobustTSF & [45] & 2024 & 0.6457 \(\pm\) 0.1156 & 0.7164 \(\pm\) 0.0747 & 0.6895 \(\pm\) 0.0825 & 0.7081 \(\pm\) 0.1323 & 0.8515 \(\pm\) 0.0592 & 0.7223 \(\pm\) 0.116 \\ \hline Proposed & & 2025 & 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 \hline \end{tabular}
\end{table} 
Table 5: Performance comparison of state-of-the-art deep learning classification models for unseen non-target gesture classification. Gaussian augmentation was included for all the models. gestures that are clearly different from target actions. In contrast, the present study focuses on the harder scenario where non-target gestures exhibit similar spatial or temporal structures to the target classes. This setting reflects practical scenarios where non-target actions may not be entirely novel but rather slight variations of known gestures.