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

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%.

Contactless Blood Oxygen Saturation Estimation from Facial Videos Using Deep Learning

“…Over the last decade, many deep learning-based methods have been developed for remote vital sign monitoring, with many studies focusing on HR [ 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 ], followed by RR [ 36 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 ]. In general, the underlying principle behind these methods is remote photoplethysmography (rPPG).…”

“…Researchers have used a variety of cameras, from infrared cameras [ 14 ] and high-quality monochrome cameras equipped with special filters [ 15 , 16 , 17 , 18 ] to off-the-shelf webcams [ 19 , 20 , 21 , 22 , 23 ], to estimate SpO 2 by capturing subtle light intensity changes on the face. Deep learning techniques have achieved state-of-the-art performance for the remote measurement of physiological signs such as HR [ 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 ] and RR [ 36 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 ]. However, remote SpO 2 measurement is still in its infancy, with only a few papers using convolutional neural networks (CNNs) to predict SpO 2 from RGB facial videos [ 48 , 49 , 50 ].…”

Smart Healthcare: Exploring the Internet of Medical Things with Ambient Intelligence

“…In the case of blood gas sensors, accuracy can be increased through SMOTEENN sampling [41], kernel principal component analysis (KPCA), and adaptive boosting [38]. Additionally, the accuracy of respiratory rate monitoring can be enhanced through motion video magnification using the Hermite transform and an artificial hydrocarbon network (AHN) following classification of frames using Bayesian-optimized AHN [42].…”