## 4 Discussion  
### Performance Analysis  
The model's Ac was consistent across interval lengths and overlap settings (Table 3), indicating that the classifier's ability to correctly label both classes (0 and 1) is not significantly affected by these parameters. The F1 score, the harmonic mean of the precision and Se, followed a similar pattern to that of Se, declining with higher overlaps within longer intervals.  
The 10 s interval length with a 50% overlap consistently outperformed other features, achieving the highest scores in terms of Ac (87.85%), precision (91.75%), Se (84.42%), and the F1 score (86.63%), making it the optimal setup for ECG signal segmentation.  
Despite expectations, lower overlap intervals showed superior performance, while poorer results occurred with higher overlap percentages and shorter interval lengths, with a 7 s interval and 90% overlap yielding the lowest scores in terms of Ac (85.41%), Se (80.48%), and the F1 score (83.59%).  
While these results demonstrate strong classification performance, it is crucial to place them in the context of existing research. However, ECG SQA in wearable conditions remains an underexplored area, making direct comparisons challenging. As highlighted in Section 1.3, none of the reviewed studies incorporated activity-annotated ECG recordings allowing for SQA across different motion contexts.  
We compared our findings with the studies described in Section 1.3.4, as these works also focused on ECG quality assessment in wearable systems. [PERSON] [18] achieved an accuracy of 92.5% in a controlled setting using a wearable ECG belt and patch, but their dataset did not include real-world motion noise, limiting its applicability to dynamic environments. [PERSON] et al. [10] obtained a higher accuracy of 95.8% using a Lenovo H3 wearable ECG device and LSTM; however, their approach relied on cardiologist-labeled data, whereas our method does not depend on expert-labeled ground truth, making it more adaptable for real-time applications. [PERSON] et al. [11] developed an unsupervised ECG quality assessment method for a SensEcho wearable device, achieving 94.97% accuracy with an Isolation Forest algorithm. While effective, their approach primarily focused on anomaly detection rather than ECG quality assessment, which was the key objective of our study.  
Despite achieving lower accuracy than some deep learning-based studies, our approach demonstrated strong classification performance with an accuracy of 87.85% while using only eight extracted features. This makes it computationally efficient and well suited for real-time wearable applications. Additionally, unlike previous work conducted under controlled conditions, our study accounted for motion-induced noise and real-world variability, ensuring its relevance for dynamic environments such as firefighter health monitoring. Another key contribution of our work is the analysis of the segment length and overlap percentages, a parameter rarely explored in wearable ECG classification research. By integrating these comparative insights, we reinforce the uniqueness of our study and highlight the need for further research in wearable ECG quality assessment, particularly in high-noise, real-world settings such as firefighter operations.