Energy Expenditure Estimation From Respiratory Magnetometer Plethysmography: A Comparison Study.

Physical activity (PA) quantification by estimating energy expenditure (EE) is essential to health. Reference methods for EE estimation often involve expensive and cumbersome systems to wear. To address these problems, light-weighted and cost-effective portable devices are developed. Respiratory magnetometer plethysmography (RMP) is among such devices, based on the measurements of thoraco-abdominal distances. The aim of this study was to conduct a comparative study on EE estimation with low to high PA intensity with portable devices including the RMP. Fifteen healthy subjects aged 23.84±4.36 years were equipped with an accelerometer, a heart rate (HR) monitor, a RMP device and a gas exchange system, while performing 9 sedentary and physical activities: sitting, standing, lying, walking at 4 and 6 km/h, running at 9 and 12 km/h, biking at 90 and 110 W. An artificial neural network (ANN) as well as a support vector regression algorithm were developed using features derived from each sensor separately and jointly. We compared also three validation approaches for the ANN model: leave one out subject, 10 fold cross-validation, and subject-specific. Results showed that 1. for portable devices the RMP provided better EE estimation compared to accelerometer and HR monitor alone; 2. combining the RMP and HR data further improved the EE estimation performances; and 3. the RMP device was also reliable in EE estimation for various PA intensities.

Energy Expenditure Estimation in Children, Adolescents and Adults by Using a Respiratory Magnetometer Plethysmography System and a Deep Learning Model.

PURPOSE: Energy expenditure is a key parameter in quantifying physical activity. Traditional methods are limited because they are expensive and cumbersome. Additional portable and cheaper devices are developed to estimate energy expenditure to overcome this problem. It is essential to verify the accuracy of these devices. This study aims to validate the accuracy of energy expenditure estimation by a respiratory magnetometer plethysmography system in children, adolescents and adults using a deep learning model. METHODS: Twenty-three healthy subjects in three groups (nine adults (A), eight post-pubertal (PP) males and six pubertal (P) females) first sat or stood for six minutes and then performed a maximal graded test on a bicycle ergometer until exhaustion. We measured energy expenditure, oxygen uptake, ventilatory thresholds 1 and 2 and maximal oxygen uptake. The respiratory magnetometer plethysmography system measured four chest and abdomen distances using magnetometers sensors. We trained the models to predict energy expenditure based on the temporal convolutional networks model. RESULTS: The respiratory magnetometer plethysmography system provided accurate energy expenditure estimation in groups A (R2 = 0.98), PP (R2 = 0.98) and P (R2 = 0.97). The temporal convolutional networks model efficiently estimates energy expenditure under sitting, standing and high levels of exercise intensities. CONCLUSION: Our results proved the respiratory magnetometer plethysmography system's effectiveness in estimating energy expenditure for different age populations across various intensities of physical activity.

Ventilatory responses at submaximal exercise intensities in healthy children and adolescents during the growth spurt period: a semi-longitudinal study.

PURPOSE: To identify the changes of ventilation ([Formula: see text]E), tidal volume (VT) and respiratory frequency (fr) at different incremental step test intensities during maturation of children and adolescents. METHODS: A semi-longitudinal study was conducted on 68 healthy untrained boys and girls aged 11-17 years. The subjects were separated into three distinct age groups. [Formula: see text]E, VT and fr parameters were evaluated annually during 3 years by modifying incremental step test intensities according to ventilatory threshold (VTh) level (30, 60 and 90% of [Formula: see text]O2max). Absolute and relative values of ventilatory responses were analyzed and compared according to age and developmental phase. RESULTS: (1) Height, weight, lean body mass and vital capacity increased significantly from 11 to 17 years of age. (2) [Formula: see text]O2max, [Formula: see text]E, and VT increased during maturation even when exercise intensity changed, especially from 11 to 15 years of age. On the other hand, fr showed a decreasing trend. CONCLUSION: Increases of VT are the main reason for [Formula: see text]E increases during maturation of children. fr decreased independently of total body mass during maturation. [Formula: see text]E.kg-1 was stable despite intensity variations. VT.kg-1 increased significantly from 11 to 15 years then stabilized at 17 years. Lean body mass seems to explain the evolution of VT.kg-1 during maturation.

A novel algorithm for minute ventilation estimation in remote health monitoring with magnetometer plethysmography.

PURPOSE: The purpose of this study was to evaluate the accuracy of minute ventilation (V˙E) 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 reference V˙E measurements. An artificial neural network (ANN) model based on respiration features was used to estimate V˙E 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 the V˙E 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 the V˙E 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.

Estimation of respiratory variables from thoracoabdominal breathing distance: a review of different techniques and calibration methods.

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.