## 6 Discussion
The resultant figures were self-explanatory in terms of statistical significance. All of these studies demonstrate the potential for the application of machine learning trained on RIP data based on their accuracy figures. Respiratory rates and volumes detected with RIP show generally good agreement with spirometry results for both stationary and ambulatory scenarios, and light to moderate exercise does not seem to affect the usability of RIP, except in [35; 49]. However, upon subjective inspection of RIP measurements, errors were greater for a few subjects in this regard, deviating from averaged aggregated results. For example, \(\geq\)93% and \(\geq\)99% of results remained within the \(\pm\)10% and \(\pm\)20% error limits, respectively, in [45]. The \(\sim\)6% resultslying within the \(\pm\)10%-\(\pm\)20% error range is a concern for practical usability, which may result in unreliable outcomes in clinical practices. Moreover, results lying within the \(\pm\)10% error range might be unusable for some clinical procedures, e.g., diagnosis of obstructive lung diseases, as FEV\({}_{1}\), FVC, respiratory rate, and tidal volume measurements are required to be precise and accurate in such cases. Other selected studies [9; 35; 46; 47; 48; 50; 66] showed strong correlations between RIP and spirometry in this regard (0.80\(\geq\) r \(\geq\)1.00), except for [49]; however, some degree of measurement error existed in all of these studies. On the other hand, [34] achieved a flow-rate detection accuracy \(>\)99% using a custom algorithm developed by the authors. There is no conclusive evidence of an acceptable error range for measurement of VC, FEV\({}_{1}\), FVC, and FEV\({}_{1}\)/FVC ratios in any of the selected studies for diagnostic purposes. Therefore, we do not recommend RIP as an alternative to spirometry for clinical and diagnostic purposes. The variation in RIP measurement errors might occur due to the data processing techniques used and the inclusion or exclusion of selected data based on signal quality. Moreover, none of the selected studies specified the gender-wise performance of RIP, thus, the gender-based variability in RIP measurements remains unclear. Additionally, the RIP data collection and extraction procedure for the measurement of respiratory volumes may not be more suitable than spirometry in practical conditions due to the necessity of strict adherence to the study protocols. The study [27] was conducted on three subjects of 30 \(\pm\) 2 years of age, which limits the acceptability of the results for a broader range of subject age groups. The studies [35; 44] only measured respiratory rates and did not measure respiratory volumes. Subjects were found to be healthy in all of the studies except for [46], while [44; 45] did not mention subject health status. RIP measurements were validated in high-BMI subjects (BMI \(\geq\)30) against a control group in [43], whereas the authors of [39] found low or insignificant agreement between RIP and spirometry when a control group and patients with obesity hypoventilation syndrome were compared. It is our opinion that further investigation is necessary to validate RIP measurements across the whole BMI range in both healthy and disease conditions. Upon analyzing the results synthesis for the selected studies, RIP appears to perform better when the belts are sewn or embedded into wearable garments. The reasons lie in the better adherence of RIP belts to the surface of the body, as well as retained placement of the belts throughout the experimental procedure, as suggested by the evidence. Custom-sized belts in wearable garments should provide better subject-wise calibration, which may reduce errors to a great extent, as suggested in [27; 46]. Thus, we prefer wearable RIP systems to the conventional dual RIP belt setup if the conditions are suitable and accuracy is crucial, as there might be an increased cost associated with such implementations of RIP.