## 4 Discussion  
This study integrates FOT with continuous EQV sensor monitoring to enable complementary information into the assessment of respiratory mechanics. The primary objective was to develop a comprehensive framework that integrates into a feasible ambulatory protocol the strengths of FOT and EQV technologies to improve respiratory monitoring. The findings of this study offer a promising framework for advancing non-invasive respiratory mechanics assessments using a combination of low-frequency FOT and continuous monitoring with wearable sensors. Future research could explore the application of this integrated approach in clinical settings, particularly for the early diagnosis and management of chronic respiratory conditions such as COPD and idiopathic pulmonary fibrosis. Additionally, the potential to incorporate artificial AI tools for real-time data analysis and forecasting opens new avenues for predictive diagnostics. This study has laid the groundwork for further exploration into the role of smart textiles and continuous physiological monitoring in personalized healthcare for wearable health technology, which continues to evolve. Expanding this work to include diverse patient populations and different respiratory conditions will be essential for validating its clinical utility and enhancing its effectiveness in long-term monitoring and disease management. In this study, we compared different models for respiratory mechanics assessments, including the fractional-order impedance model and the Cole-Cole model, to determine which model best fits the data collected through both FOT and EQV continuous monitoring. While previous studies have utilized FOT for respiratory mechanics, our integration of the EQV wearable sensor system for continuous monitoring introduces a novel approach that has not been previously explored. The EQV system enables continuous tracking of physiological signals in real-life, ambulatory conditions, offering an advantage over traditional methods that rely on short-term, isolated measurements. Although there are no directly comparable studies using this combination of technologies, our results indicate that the EQV-FOT integration holds significant potential for enhancing respiratory assessments, particularly for long-term monitoring. Additionally, the incorporation of AI tools for real-time analysis further strengthens the utility of this approach by enabling predictive modeling and more personalized assessments [32]. The findings suggest that EQV, combined with FOT calibration, could serve as a robust tool for ambulatory respiratory monitoring and open new research directions in the field. One of the limitations of FOT is the potential discomfort and impracticality of long-term use. While FOT provides detailed measurements of respiratory impedance, its application in continuous monitoring is limited due to the need for frequent and often intrusive measurements. This study addresses these limitations by integrating EQV sensors, which offer continuous, non-invasive monitoring of vital signals such as heart rate, respiratory rate, and skin temperature. Utilizing EQV for continuous monitoring reduces the need for repeated FOT measurements, thereby enhancing patient comfort and compliance. The ability to continuously monitor vital signals using EQV sensors and correlate them with impedance model parameters, providing a robust framework for the real-time assessments of lung function. Moreover, the use of AI tools to analyze the obtained datasets from FOT and EQV highlights the potential to significantly enhance predictive capabilities, seeming to be a good candidate to continuously monitor patients for long time periods. AI algorithms based on time signals and correlations, as well as time-frequency image-based data such as spectrograms, can identify patterns and correlations that may not be apparent through traditional analyses.