## 1 Introduction  
The accurate assessment of respiratory mechanics is fundamental for diagnosing and managing various lung diseases. Traditional methods, such as spirometry, provide valuable information but are limited in their ability to provide continuous monitoring since they require continuous patient cooperation in forced maneuvers, which can be challenging for some individuals [1; 2; 3; 4]. The Forced Oscillation Technique (FOT) has emerged as a valuable alternative, providing non-invasive measurements of respiratory impedance by applying oscillatory pressure waves during normal breathing conditions to the respiratory system and measuring the resultant airflow and pressure responses.  
Extensive research has been conducted on respiratory impedance and its clinical implications, leading to the development of advanced monitoring systems. Low-frequency FOT (0.1-2 Hz) has proven to be effective in reflecting disease stages and predicting deterioration in conditions such as idiopathic pulmonary fibrosis [5]. Comparative studies of forced oscillometry and other measurement techniques have highlighted the strengths and limitations of these methods, guiding their optimization [6; 7; 8; 9; 10; 11; 12].  
Recent advancements have focused on low-frequency FOT to enhance understanding of the mechanical behavior of lung tissues and peripheral airways [5; 13; 14]. This frequency range is sensitive to changes in compliance and resistance, making it particularly useful for detecting early alterations in lung function [3; 4; 15]. Innovations such as adaptive excitation signals have further improved the precision and reliability of low-frequency FOT measurements in various clinical settings [2].  
In addition, high-frequency FOT (20-200 Hz) provides complementary insights by assessing different mechanical properties of the respiratory system. At these frequencies, impedance is more influenced by central airway properties and inertial effects, offering a broader perspective [10; 13; 16]. Despite its potential, the continuous use of FOT in clinical practice is hindered by challenges such as patient discomfort and the impracticality of long-term measurements.  
To address these challenges, this study integrates low-frequency FOT with continuous monitoring using Equivital (EQV) sensors in smart textiles. Sensors, integrated within these textiles, can continuously track key physiological indicators, such as heart rate, respiratory rate, and skin temperature, providing a comprehensive overview of a patient's condition without the need for invasive procedures [17; 18]. This integration aims to enhance respiratory mechanics assessment by leveraging the strengths of both FOT and EQV technologies, ultimately improving patient outcomes and advancing respiratory monitoring [19]. By measuring respiratory impedance every 7 min over a 2 h period and continuously recording physiological data with EQV sensors, we aim to identify the best-fitting fractional-order models for impedance data using genetic algorithms. Additionally, we explore the correlation between impedance model parameters and EQV data, and discuss the potential of using AI tools for forecasting respiratory mechanics based on these integrated datasets.  
This paper is organized as follows: In Section 2, we detail the methodology used for the experiments, including the design of the FOT and continuous monitoring setup with the EQV sensor. Section 3 presents the experimental results, focusing on the comparison between different models of respiratory mechanics and the correlation between impedance data and physiological parameters. In Section 4, we discuss the implications of these results, including the potential for integrating AI tools to enhance respiratory assessment. Finally, Section 5 concludes this paper, summarizing the key findings and proposing directions for future research.