Rémy Dumond1,2, Steven Gastinger3,4, Hala Abdul Rahman3,5, Alexis Le Faucheur3,6, Patrice Quinton3,7, Haitao Kang8, Jacques Prioux9,10. 1. Laboratoire Mouvement, Sport, Santé (EA 1274), Université de Rennes 2, Avenue Robert Schuman, 35170, Bruz, France. remy.dumond@gmail.com. 2. Département Sciences du sport et éducation physique, Ecole normale supérieure de Rennes, Campus de Ker Lann, Avenue Robert Schuman, 35170, Bruz, France. remy.dumond@gmail.com. 3. Laboratoire Mouvement, Sport, Santé (EA 1274), Université de Rennes 2, Avenue Robert Schuman, 35170, Bruz, France. 4. APCoSS, Institut de Formation en Éducation Physique et en Sport d'Angers (IFEPSA), Les Ponts de Cé, France. 5. Laboratoire du Traitement du Signal et de l'Image, Université de Rennes 1, Campus de Beaulieu, Bâtiment 22, Rennes, 35042 Cedex, France. 6. Département Sciences du sport et éducation physique, Ecole normale supérieure de Rennes, Campus de Ker Lann, Avenue Robert Schuman, 35170, Bruz, France. 7. Departement Informatique et télécommunications, Ecole normale supérieure de Rennes, Campus de Ker Lann, Avenue Robert Schuman, 35170, Bruz, France. 8. Yuewu Electronic Technology Co., Ltd, Room 1008, Building B, No. 2305, Zuchongzhi Road, Shanghai, 201203, China. 9. Laboratoire Mouvement, Sport, Santé (EA 1274), Université de Rennes 2, Avenue Robert Schuman, 35170, Bruz, France. jacques.prioux@ens-rennes.fr. 10. Département Sciences du sport et éducation physique, Ecole normale supérieure de Rennes, Campus de Ker Lann, Avenue Robert Schuman, 35170, Bruz, France. jacques.prioux@ens-rennes.fr.
Abstract
PURPOSE: The purposes of this study were to both improve the accuracy of respiratory volume (V) estimates using the respiratory magnetometer plethysmography (RMP) technique and facilitate the use of this technique. METHOD: We compared two models of machine learning (ML) for estimating [Formula: see text]: a linear model (multiple linear regression-MLR) and a nonlinear model (artificial neural network-ANN), and we used cross-validation to validate these models. Fourteen healthy adults, aged [Formula: see text] years participated in the present study. The protocol was conducted in a laboratory test room. The anteroposterior displacements of the rib cage and abdomen, and the axial displacements of the chest wall and spine were measured using two pairs of magnetometers. [Formula: see text] was estimated from these four signals, and the respiratory volume was simultaneously measured using a spirometer ([Formula: see text]) under lying, sitting and standing conditions as well as various exercise conditions (working on computer, treadmill walking at 4 and 6 km[Formula: see text], treadmill running at 9 and 12 km [Formula: see text] and ergometer cycling at 90 and 110 W). RESULTS: The results from the ANN model fitted the spirometer volume significantly better than those obtained through MLR. Considering all activities, the difference between [Formula: see text] and [Formula: see text] (bias) was higher for the MLR model ([Formula: see text] L) than for the ANN model ([Formula: see text] L). CONCLUSION: Our results demonstrate that this new processing approach for RMP seems to be a valid tool for estimating V with sufficient accuracy during lying, sitting and standing and under various exercise conditions.
PURPOSE: The purposes of this study were to both improve the accuracy of respiratory volume (V) estimates using the respiratory magnetometer plethysmography (RMP) technique and facilitate the use of this technique. METHOD: We compared two models of machine learning (ML) for estimating [Formula: see text]: a linear model (multiple linear regression-MLR) and a nonlinear model (artificial neural network-ANN), and we used cross-validation to validate these models. Fourteen healthy adults, aged [Formula: see text] years participated in the present study. The protocol was conducted in a laboratory test room. The anteroposterior displacements of the rib cage and abdomen, and the axial displacements of the chest wall and spine were measured using two pairs of magnetometers. [Formula: see text] was estimated from these four signals, and the respiratory volume was simultaneously measured using a spirometer ([Formula: see text]) under lying, sitting and standing conditions as well as various exercise conditions (working on computer, treadmill walking at 4 and 6 km[Formula: see text], treadmill running at 9 and 12 km [Formula: see text] and ergometer cycling at 90 and 110 W). RESULTS: The results from the ANN model fitted the spirometer volume significantly better than those obtained through MLR. Considering all activities, the difference between [Formula: see text] and [Formula: see text] (bias) was higher for the MLR model ([Formula: see text] L) than for the ANN model ([Formula: see text] L). CONCLUSION: Our results demonstrate that this new processing approach for RMP seems to be a valid tool for estimating V with sufficient accuracy during lying, sitting and standing and under various exercise conditions.
Authors: Martin Kriegel; Anne Hartmann; Udo Buchholz; Janna Seifried; Sigrid Baumgarte; Petra Gastmeier Journal: Int J Environ Res Public Health Date: 2021-12-25 Impact factor: 3.390