Non-Contact Breathing Rate Estimation Using Machine Learning with an Optimized Architecture

Jorge Brieva (), Hiram Ponce and Ernesto Moya-Albor ()
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Jorge Brieva: Facultad de Ingeniería, Universidad Panamericana, Augusto Rodin 498, Ciudad de México 03920, Mexico
Hiram Ponce: Facultad de Ingeniería, Universidad Panamericana, Augusto Rodin 498, Ciudad de México 03920, Mexico
Ernesto Moya-Albor: Facultad de Ingeniería, Universidad Panamericana, Augusto Rodin 498, Ciudad de México 03920, Mexico

Mathematics, 2023, vol. 11, issue 3, 1-23

Abstract: The breathing rate monitoring is an important measure in medical applications and daily physical activities. The contact sensors have shown their effectiveness for breathing monitoring and have been mostly used as a standard reference, but with some disadvantages for example in burns patients with vulnerable skins. Contactless monitoring systems are then gaining attention for respiratory frequency detection. We propose a new non-contact technique to estimate the breathing rate based on the motion video magnification method by means of the Hermite transform and an Artificial Hydrocarbon Network (AHN). The chest movements are tracked by the system without the use of an ROI in the image video. The machine learning system classifies the frames as inhalation or exhalation using a Bayesian-optimized AHN. The method was compared using an optimized Convolutional Neural Network (CNN). This proposal has been tested on a Data-Set containing ten healthy subjects in four positions. The percentage error and the Bland–Altman analysis is used to compare the performance of the strategies estimating the breathing rate. Besides, the Bland–Altman analysis is used to search for the agreement of the estimation to the reference.The percentage error for the AHN method is 2.19 ± 2.1 with and agreement with respect of the reference of ≈99%.

Keywords: machine learning; convolutional neural networks; artificial hydrocarbon networks; Hermite transform; motion video magnification; non-contact monitoring; respiratory rate estimation; preventable death; health (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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