# Gesture Classification Using a Smartwatch: Focusing on Unseen Non-Target Gestures
## 5 Conclusions  
The classification of unseen non-target data refers to a model's ability to distinguish untrained movements that resemble common daily actions. This capability is essential in real-world gesture recognition systems, especially in asynchronous settings where unintended activations must be prevented.  
To address this challenge, this study designed an experiment that focused on classifying unseen non-target movements using motion data. Unlike previous studies that primarily focused on target recognition or used conventional target gestures as non-target data, this study explicitly designed unseen non-targets: five non-target patterns corresponding to each of the five target movements. Through these experiments, we aimed to assess the model's ability to generalize classification criteria beyond the predefined training set. The proposed method was validated using arm movement data from 20 participants in the target group and 11 in the non-target group, achieving an average F1-score of 84.23%, with a non-target classification score of 73.23%.  
The proposed approach demonstrated the ability to recognize untrained movements, enabling the model to adapt to novel actions and unexpected scenarios. The results also showed that non-target gestures resembling target gestures are prone to misclassification, which can be mitigated by data augmentation and a loss factor that emphasizes non-target filtering. Additionally, the choice of augmentation method is critical, as certain techniques may distort training signals and reduce performance on unseen data.  
Future research should aim to improve the classification of unseen non-target patterns by addressing the limitations of training diversity. Although all unseen patterns in this study shared the characteristic of smooth movement, the proposed model struggled to distinguish some signals from target signals. A deeper understanding of this issue could be achieved by further investigating the impact of different movement patterns on classification performance. In addition, the non-target patterns used in this study were defined as smoothed versions of target movements, intentionally removing pauses and angular changes. While this design emphasizes subtle differences, it does not fully reflect the variability of natural movements that may be exhibited by real users. Future work should consider incorporating more diverse and unconstrained non-target gestures to better simulate real-world behavior.  
**Author Contributions:** Conceptualization, W.-D.C.; Methodology, J.-H.C., H.-T.C., J.-S.J., S.-H.L. and W.-D.C.; Software, J.-H.C., K.-T.K. and W.-D.C.; Data curation, J.-S.J. and S.-H.L.; Writing--original draft, J.-H.C., H.-T.C., K.-T.K. and W.-D.C.; Writing--review & editing, W.-D.C.; Funding acquisition, W.-D.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. RS-2024-00334159).  
The study protocol was approved by the Institutional Review Board (IRB) of Pukyong National University (#1041386-202104-HR-12-01).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
Author [PERSON] was employed by the company Nexen Tire Research Center. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.