# Gesture Classification Using a Smartwatch: Focusing on Unseen Non-Target Gestures
## 3 Results  
The classification performance of the proposed model was evaluated using precision, recall, and F1-score metrics (Table 3). The results indicate that the model achieved an overall precision, recall, and F1-score of 0.8592, 0.8459, and 0.8423, respectively. When considering only target patterns, the model exhibited an average precision of 0.8497, recall of 0.8885, and F1-score of 0.8643. In contrast, for non-target patterns, the model achieved a higher precision of 0.9071 but significantly lower recall (0.6327) and F1-score (0.7323). This discrepancy suggests that while the model is effective in maintaining high precision for non-target patterns, it tends to exhibit lower recall, implying that some non-target patterns are misclassified as target patterns. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Training**} & \multicolumn{2}{c}{**Test**} & \multicolumn{2}{c}{**Accuracy (Averaged over 10 Repetition)**} \\ \hline
**Target Patterns** & **Non-Target Patterns** & **Non-Target Patterns** & **Precision** & **Recall** & **F1 Score** \\ \hline \hline \multirow{8}{*}{All except FRU-N} & FRU & 0.7698 & 0.8636 & 0.8107 \\ \cline{2-5}  & FRD & 0.8487 & 0.8879  
A detailed analysis of individual test conditions reveals variations in model performance across different excluded non-target patterns. The model's recall is notably lower when classifying the TRA-N pattern, with a value of 0.3758, resulting in an F1-score of 0.5141. This phenomenon appears to be attributed to the unique characteristics of the TRA pattern. Unlike other movement patterns, which follow distinct directional shifts, the TRA pattern is designed with a continuous circular motion. Consequently, when the TRA-N pattern is excluded from the training data, the model struggles to classify it accurately during testing. In contrast, other non-target patterns maintain a similar fundamental movement style, differing only in direction, making their classification less challenging. Among target patterns, the model performed consistently well, achieving the highest F1-score of 0.9728 for the TRA movement, while the lowest F1-score was observed for the FLU movement (0.8379). The model's performance on unseen non-target patterns also varied depending on the excluded class, with the highest F1-score (0.8016) observed for the FLD-N pattern and the lowest (0.5141) for TRA-N.  
To further evaluate the classification performance of the proposed model, confusion matrices were analyzed across the five different experimental settings. Each setting was validated ten times (Figure 5). The rows indicate the actual class labels, while the columns represent the predicted class labels. Figure 5: _Cont._  
The overall results indicate that the model effectively classifies target movements, with most instances correctly assigned to their respective target categories. However, variations in misclassification patterns were observed depending on the excluded non-target pattern. In particular, the model demonstrated strong classification performance for the TRA movement, consistently achieving high accuracy across all settings. The distinct motion characteristics of this pattern likely contribute to its robust classification. Conversely, FLU and FRU exhibited relatively higher misclassification rates, primarily due to their similar movement trajectories. This pattern of confusion suggests that the model struggles to distinguish between certain target movements when they exhibit overlapping motion characteristics. Figure 6 presents the accuracy and loss curves for both the training and validation phases. Although an increase in validation loss was observed, this does not necessarily indicate deteriorating performance. The rise in loss could occur due to correct predictions made with low confidence. The smooth convergence of the accuracy curves supports the model's stability throughout training. Table 4 presents the classification performance of the proposed model with different data augmentation methods.