## 5 Artificial Intelligence-Based Applications of RIP  
The integration of machine learning (ML) and artificial intelligence (AI) with RIP marks a significant advancement in the field of respiratory medicine, where feature extraction from RIP data can reveal hidden information to identify a variety of both physiological and pathological conditions. The utilization of advanced technologies allows for a more detailed and thorough examination of breathing patterns. An important development in this field involves the study and application of non-linear computational models to enhance the calculation of respiratory volume based on thoracoabdominal motions with the use of RIP. The shift towards employing machine learning (ML) models, such as artificial neural networks (ANNs) and adaptive neuro-fuzzy inference systems (ANFISs), is a divergence from the linear calibration approaches formerly used. These models have shown an impressive ability to greatly improve the accuracy and dependability of respiratory volume estimations. In comparative research [86], advanced non-linear approaches were compared to standard linear models, demonstrating the improved efficacy of machine learning algorithms in accurately representing the intricate dynamics present in respiratory patterns. This study highlighted the capacity of machine learning (ML) to offer a more dependable and accurate assessment of respiratory function, which is essential for the efficient monitoring and control of respiratory well-being. Moreover, the extensive applicability of machine learning in respiratory medicine is demonstrated by the creation of sophisticated diagnostic instruments for respiratory illnesses. In [87], the authors highlighted the crucial significance of artificial intelligence (AI) in examining lung images for the purpose of diagnosing disorders like asthma and chronic obstructive pulmonary disease (COPD). This potential advancement in automating and precisely analyzing pulmonary function tests (PFTs) and RIP data signifies a big stride forward. Integrating RIP and machine learning (ML) could improve the diagnostic process and enable personalized treatment plans, leading to optimized patient care and better outcomes. In [38], the authors presented a revolutionary approach for calibrating Respiratory Inductive Plethysmography (RIP) using machine learning (ML) and artificial intelligence (AI) in the field of respiratory medicine without requiring ground truth, i.e., spirometry. This method allows for the integration of anthropometric data without the requirement for collecting RIP and flowmeter data simultaneously. However, this technique is still in the experimental phase and requires further improvement. In [88], the authors studied the correlation between data from a smart mask and a specially designed RIP sensor, in which the RIP coils are knitted into a wearable garment. The resultant graphs showed good agreement between predicted CO\({}_{2}\) levels and CO\({}_{2}\) data from the smart mask. Such integration of RIP sensors into wearable garments is anticipated to improve convenience and user comfort to a greater degree. In [89; 90], the authors studied machine learning-based sleep stage classification. Data packet loss in RIP signals was taken into account in [89], and 76% accuracy with a Cohen's kappa of 0.54 was achieved when sleep stages were classified into three categories. On the other hand, ref. [90] utilized ECG and body movement data alongside abdominal RIP signals and achieved 84.3% accuracy and a 0.8 F1 score. Table 7 details the synthetic overview, whereas Table 8 presents the results synthesis of the selected studies for machine learning based applications of RIP.