# Gesture Classification Using a Smartwatch: Focusing on Unseen Non-Target Gestures
## 4 Discussion  
The results demonstrate that the model was able to generalize well to unseen non-target movements while maintaining high classification accuracy for target patterns. This implies that the model may have effectively learned the characteristics of non-target movements, which often exhibit smooth curving trajectories. The use of Bi-LSTMs to capture long-term temporal dependencies in movement patterns and convolutional modules to extract local features may have contributed to a more balanced representation of both target and non-target patterns. Additionally, the target loss function introduced to distinguish between target and non-target patterns is likely to have been effective, leading to improved classification performance. However, the reduced recall for certain non-target patterns suggests that further refinement is necessary. Such refinement could enhance the model's capability to distinguish between target and non-target classes more effectively. An analysis of the confusion matrices revealed that classification performance on non-target movements varied significantly depending on the excluded class. In most cases, non-target movements were frequently misclassified as target movements, indicating a challenge in distinguishing between these categories. This trend was particularly evident for the TRA-N pattern, which showed the lowest recall among non-target movements. A considerable number of TRA-N samples were classified as target movements, supporting the earlier observation that TRA-N is inherently difficult to separate from target gestures. This difficulty likely arises due to the unique circular trajectory of TRA, which differs from the directional shifts observed in other patterns. When TRA-N was excluded from the training data, the model struggled to establish a clear distinction, leading to a high misclassification rate. Consequently, the absence of this pattern in training data severely hindered the model's ability to generalize its classification criteria. These findings highlight several key insights regarding the model's classification behavior. While the model demonstrates strong classification performance for most target patterns, it faces challenges in distinguishing non-target movements, particularly when they share trajectory similarities with target movements. The variation in confusion matrices across different experimental conditions underscores the impact of the excluded non-target pattern on overall classification performance. This suggests that the model relies on learned representations that are influenced by the specific set of training samples. Table 5 compares the F1 scores of well-known state-of-the-art deep learning models and the proposed model for unseen non-target gesture classification. To adapt image-based models such as VGG16 and ConvVNext for 1D data, the original 2D convolutional layers were replaced with 1D convolutions. Additionally, Gaussian augmentation was applied to all models to enhance their robustness. Most traditional models demonstrated relatively lower performance in classifying FLD, FLU, FRD, and FRU compared to TRA. This trend indicates that conventional models tend to classify completely different patterns from the seen classes as out-of-distribution, while their classification accuracy decreases when encountering patterns that resemble the target gestures. This phenomenon suggests that models struggle to distinguish between non-target gestures that share similar characteristics with the target classes. Furthermore, even the recently proposed models tailored for 1D signals, such as MordenTCN and RobustTSF, exhibited a similar tendency. RobustTSF, which is known for its robustness against noise and outliers, also showed reduced accuracy when classifying non-target gestures resembling the target classes. The proposed model, however, achieved a 1.0%p higher average accuracy compared to RobustTSF. Unlike conventional models, the proposed approach demonstrated better performance in identifying unseen data that closely resemble target gestures.