## 6 Discussion

Table 8: Results synthesis for artificial intelligence-based applications of RIP. breathing between thorax and abdomen. However, usage of RIP is not restricted to the conventional scope of spirometry and extends to the monitoring of some cardiovascular activities as well. Through carefully pre-processing raw RIP data, statistical analyses and ML-based approaches have been used to extract relevant information in studies. All of the included studies validated their results and outcomes by implementing parallel methods, often gold standards within the corresponding fields. Several applications of RIP have been identified in these studies, e.g., the validation of RIP in subjects in both stationary and ambulatory cases, monitoring of infants' breathing in non-ideal conditions, diagnosis of sleep apnea, classification of sleep stages, calibration procedures for RIP, and recognition of human activity through conventional statistical analysis and the application of machine learning where applicable. Studies [9; 34; 35] validated the Hexoskin smart shirt through the measurement of correlations with spirometry and percentages of measurement error, which demonstrated its potential for use in practical conditions. Study [10] compared pneuRIPTM with the Respiratory system, and the percentage error demonstrated the superiority of the pneuRIPTM system in the experiment. Studies [23; 51; 65] expressed the magnitude of breathing asynchrony in phase-angle degrees, where a 180\({}^{\circ}\) phase angle indicates complete asynchrony between thoracic and abdominal breathing. Studies [12; 53] expressed the detection of apnea and hypopnea in terms of specificity and sensitivity figures, which indicate false-positive and false-negative results, respectively. Studies [44; 45] expressed their results in percentage error ranges, and the measurements showed an error range between 5% and 20% in most cases. In [27], the errors were expressed in root-mean-square (RMS) values, which were much lower for the belt + shirt combination than for the other test conditions. Studies [43; 46; 47; 48; 50] expressed their results in terms of correlation coefficients between pneumothorax and RIP measurements, which ranged between 0.8 and 0.95, which indicated a relatively high accuracy. Study [37] adopted a support vector regression (SVR) model to compare RIP calibration methods, where a least-squares approximation (LSQ) model proved to be the most accurate. Study [13] expressed AHI scoring errors measured with RIP in terms of scoring biases with manual scoring, which showed some errors, whereas [54] expressed AHI scoring accuracy through correlation coefficients, which indicated zero error in their experiment. Study [59] demonstrated a high correlation of RIP-measured pulse wave velocities (PWVs) with brachial PWV measurements, where \(p<0.001\), which indicates the potential feasibility of RIP in this setting. Study [55] demonstrated a moderate correlation coefficient between gold-standard and RIP data and emphasized the importance of analysis of low-frequency data for sleep stage classification. Study [40] measured tidal volumes, breathing asynchrony, and percentage ribcage contributions for breathing to identify the best positioning for premature infants, where high tidal volume, low breathing asynchrony, and a higher ribcage contribution indicate a better position. Study [38] demonstrated that data collected from standing still before exercise provide a better calibration, which is indicated by a higher limit of equivalence with a flowmeter. Study [67] monitored breathing asynchrony associated with muscle fatigue, and a higher degree of asynchrony was found in cases of muscle fatigue due to repeated work. In [39], the authors found the limits of agreement of RIP with spirometry to be too high (\(\geq\)20%) to validate RIP in obese people. The studies [36; 41; 42; 52; 56; 57; 58; 59; 60; 61; 62; 63; 69; 60; 63; 61] implemented machine learning and expressed their results in terms of accuracy, precision, sensitivity, specificity, and/or Cohen's kappa figures.