## 5 Conclusions  
This study investigated the SQA of ECG signals from wearable devices, targeting data collected in firefighters, utilizing machine learning. Continuous ECG monitoring is essential for diagnosing sporadic cardiac arrhythmias that are difficult to detect with short-duration ECGs. Firefighters face elevated cardiac risks due to adverse working conditions, making the implementation of wearable sensor systems crucial for monitoring their health.  
The primary goal was to develop a supervised model using stratified 5-fold cross-validation to segment ECG signals into acceptable and unacceptable intervals, using a limited set of features. The ScientiISST MOVE dataset, which contains ECG biosignals recorded with dry electrodes during various daily activities, provided a rich source of data for this study, offering 6468 segments of 10 s durations. The model's performance was notably high, achieving an Ac of 88% and an F1 score of 87%, despite relying on only eight ECG features. This outcome highlights the potential of the selected features for the signal quality assessment of data collected in highly dynamic conditions.  
Future work should address several limitations identified in this study, namely the following:Sample Size: Expanding the sample size could improve the ability to detect more nuanced differences, such as the impact of age and gender on the ECG signal quality.
* Dataset Diversity: Incorporating a more diverse dataset with both pathological and non-pathological ECG signals, as well as varying types of noise, is essential for developing more effective signal quality models.
* Model Hyperparameters: Further exploration of the hyperparameters for RFs, such as the maximum tree depth and the number of trees, could refine the model's performance.
* Optimum Overlap: Finding the optimum overlap is crucial to reduce computational complexity.
* Overlap Percentages and Interval Lengths: Further exploration of a broader range of overlap percentages and interval lengths is necessary to optimize ECG signal segmentation.  
**Author Contributions:** Conceptualization, C.A. and H.P.d.S.; methodology, C.A.; software, C.A.; validation, C.A. and H.P.d.S.; formal analysis, C.A.; investigation, C.A.; resources, H.P.d.S.; data curation, C.A.; writing--original draft preparation, C.A.; writing--review and editing, H.P.d.S.; visualization, C.A.; supervision, H.P.d.S.; project administration, H.P.d.S.; funding acquisition, H.P.d.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by FCT--the Fundacao para a Ciencia e Tecnologia, I.P.--under project PCIF/SSO/0163/2019 SAFEFIRE (DOI: [[https://doi.org/10.54499/PCIF/SSO/0163/2019](https://doi.org/10.54499/PCIF/SSO/0163/2019)]([https://doi.org/10.54499/PCIF/SSO/0163/2019](https://doi.org/10.54499/PCIF/SSO/0163/2019)) and project UIDB/50008/2020: Instituto de Telecomunicacoes.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors on reasonable request.  
**Acknowledgments:** The two co-authors would like to thank the anonymous reviewers for their comments and suggestions, which helped to improve the quality of the manuscript.  
**Conflicts of Interest:** The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.