The precise measurement of respiratory variables, such as tidal volume, minute ventilation, and respiratory rate, is necessary to monitor respiratory status, overcome several diseases, improve patient health conditions and reduce health care costs. This measurement has conventionally been performed by breathing into a mouthpiece connected to a flow rate measuring device. However, a mouthpiece can be uncomfortable for the subject and is difficult to use for long-term monitoring. Other noninvasive systems and devices have been developed that do not require a mouthpiece to quantitatively measure respiratory variables. These techniques are based on measuring size changes of the rib cage (RC) and abdomen (ABD), as lung volume is known to be a function of these variables. Among these systems, we distinguish respiratory inductive plethysmography (RIP), respiratory magnetometer plethysmography (RMP), and optoelectronic plethysmography devices. However, these devices should be previously calibrated for the correct evaluation of respiratory variables. The most popular calibration methods are isovolume manoeuvre calibration (ISOCAL), qualitative diagnostic calibration (QDC), multiple linear regression (MLR) and artificial neural networks (ANNs). The aim of this review is first to present how thoracoabdominal breathing distances can be used to estimate respiratory variables and second to present the different techniques and calibration methods used for this purpose.

Purpose The purpose of this study was to evaluate the accuracy of minute ventilation (̇ ) estimation using a novel method based on a non-linear algorithm coupled with cycle-based features. The experiment protocol was well adapted for remote health monitoring applications by exploiting data streams from respiratory magnetometer plethysmography (RMP) during different physical activity (PA) types. Methods Thirteen subjects with an age distribution of 24.1 ± 3.4 years performed thirteen PA ranging from sedentary to moderate intensity (walking at 4 and 6 km/h, running at 9 and 12 km/h, biking at 90 W and 110 W). In total, 3359 temporal segments of 10s were acquired using the Nomics RMP device while the iWorx spirometer was used for referencė measurements. An artificial neural network (ANN) model based on respiration features was used to estimatė and compared to the multiple linear regression (MLR) model. We also compared the subject-specific approach with the subject-independent approach. Results The ANN model using subject-specific approach achieved better accuracy for thė estimation. The bias was between 0.20 ± 0.87 and 0.78 ± 3 l/min with the ANN model as compared to 0.73 ± 3.19 and 4.17 ± 2.61 l/min with the MLR model. Conclusion Our results demonstrated the pertinence of processing data streams from wearable RMP device to estimate thė with sufficient accuracy for various PA types. Due to its low-complexity and real-time algorithm design, the current approach can be easily integrated into most remote health monitoring applications coupled with wearable sensors.