## 5 Conclusions and Future Perspectives  
This study demonstrates the feasibility and advantages of integrating low-frequency FOT with continuous monitoring using Equivital (EQUV) sensors for enhanced respiratory mechanics assessments. The integration allows for continuous, non-invasive monitoring, addressing the limitations of traditional FOT methods. Our findings suggest that fractional-order models, optimized via genetic algorithms, accurately capture the mechanical properties of the respiratory system. The correlation analysis between impedance model parameters and EQV data highlights the potential of using physiological signals to help in predicting respiratory mechanics. Additionally, the application of AI tools could further enhance the efficiency and accuracy of respiratory monitoring, reducing the need for prolonged measurement sessions. This approach leverages the strengths of both FOT and EQV technologies, providing a robust framework for continuous respiratory monitoring and early detection of lung function changes, which could significantly improve patient outcomes and advance respiratory monitoring and diagnosis. Recent studies have demonstrated the utility of these techniques in various settings, highlighting their potential for broader clinical applications and integration into everyday respiratory care. Future research should explore the clinical applications of this methodology in patients with respiratory diseases to validate its efficacy and broaden its applicability. While there have been previous studies that investigated the Forced Oscillation Technique (FOT) for the assessment of respiratory mechanics, the integration of continuous monitoring using wearable Equivital sensors in this study offers a novel approach. Traditional methods have largely focused on snapshot measurements using FOT, whereas our approach enables continuous monitoring of physiological signals, such as heart rate and respiratory rate, over extended periods. This continuous monitoring provides additional insights into the dynamic nature of respiratory function in real-life conditions. Furthermore, the use of fractional-order impedance models to analyze respiratory mechanics is another distinguishing feature of our study, as this method allows for a more detailed and accurate representation of tissue properties. Compared to other techniques, our proposed method offers the potential for non-invasive, ambulatory monitoring of patients, which could lead to improved diagnosis and management of chronic respiratory conditions. These advancements, coupled with the potential application of AI tools for real-time data analysis, set this study apart from existing research and provide a foundation for future exploration in the field. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app15020751/s1](https://www.mdpi.com/article/10.3390/app15020751/s1)]([https://www.mdpi.com/article/10.3390/app15020751/s1](https://www.mdpi.com/article/10.3390/app15020751/s1)), Figures S1-S6: HR Spectrograms for Intervals 1 to 24 for subjects 1-6. Conceptualization, D.C., C.M.I., C.C. and A.D.R. ; methodology, G.B.O. and A.R.Y. ; software, C.C., D.C. and G.B.O. ; validation, G.B.O. and A.R.Y. ; formal analysis, D.C., A.D.R., C.M.I. and C.C. ; investigation, G.B.O. and A.R.Y. ; resources, C.C. and C.M.I. ; data curation, G.B.O. and D.C.; writing--original draft preparation, D.C., G.B.O., A.R.Y., E.Y. and C.M.I. ; writing--review and editing, H.F., C.M. and I.R.B. ; visualization, D.C. and G.B.O. ; supervision, D.C., C.M.I. ; project administration, C.M.I. ; funding acquisition, C.M.I. All authors have read and agreed to the published version of the manuscript. This work was financed by Ghent University's special research grant, project number 01j01619. This work was supported in part by a grant of the Romanian Ministry of Research, Innovation and Digitization, PNRR-III-C9-2022-I8, grant number 760068/23.05.2023.