## 6 Discussion
On the other hand, increased WoB parameters were observed in patients with neuromuscular diseases, though significant overlaps were present between the control and the test subjects' phase angles and labored breathing indices. Breathing asynchrony in adults has been studied with RIP in the assessment of muscle fatigue due to repetitive work [67] and COPD [65] and in children with respiratory diseases [23], and significant increases in phase angles were found in all of these cases. In the case of COPD patients, breathing asynchrony became evident during a 6 min walk test (6 MWT), which did not appear during the resting phase prior to the 6 MWT. As the baseline respiratory parameters remained the same in both the healthy subjects and the COPD patients, the differences between the workloads required to produce breathing asynchrony appear to be indicators of subjects' lung conditions. Increased phase angles were also found in children with respiratory diseases, which are also associated with increased WoB. Asynchronous breathing associated with increased WoB was observed in preterm infants with respiratory distress, for which RIP proved to be a great monitoring tool, being both convenient and non-invasive. However, calibration remains challenging in such uses of RIP due to the movement of subjects and change in positioning. It appears to us that improper calibration might be the root cause of measurement errors in RIP implementations. Though [37] found that the least-squares approximation (LSQ) method performed the best, it still produced more errors than the gold standard. Calibration of RIP devices is challenging due to the difficulties associated with determining the gain parameter of RIP signals, which usually requires a ground truth as a reference [48]. This might be the reason behind the reference-free calibration technique studied in [38] having the poorest performance. Other studies included in this work suggest that calibration is required for each subject separately prior to measurement. Moreover, change in the subject's posture and positioning may require the device to be recalibrated. The development of new calibration techniques is another area of exploration for future research regarding RIP. Faulty results are also caused by improper belt positioning and poor adherence of the belt to the body, as the systems implementing RIP in wearable garments performed the best in our results synthesis. One study [46] suggested that re-using RIP belts tends to cause a higher magnitude of measurement error, which might be due to the distortion in the inductive material embedded in the RIP belts after repeated use. Attention from researchers is required in this regard to prove the validity of such claims. Estimation of pulse wave velocity has been experimented on in one study [59]. The high correlation (r = 0.86, \(p\) < 0.001) between measurements from RIP data and ECG is promising and requires further study to establish conclusive evidence. The application of machine learning algorithms for automated analysis of RIP data has been studied in the literature and shown great potential for some unique applications. For example, changes in breathing patterns were studied by analyzing RIP data through classifier models to identify human activities. Studies [57; 58] have recruited regular smokers as subjects to identify smoking activity from RIP data. Though the \(\geq\)80% accuracy figure demonstrates great potential for practical uses, research could be conducted in the future to improve the overall precision and F1 scores. The study [36] achieved \(>\)95% accuracy in recognizing human activity; however, the experimental protocol was limited to 10 tasks. No alternative to artificial intelligence (AI) for human activity recognition (HAR) was identified in our investigation. The accuracy figures suggest that training with a larger dataset could improve the accuracy and precision of HAR in the near future.