A Real-Time Respiration Monitoring and Classification System Using a Depth Camera and Radars

Abstract

Respiration rate (RR) and respiration patterns (RP) are considered early indicators of physiological conditions and cardiorespiratory diseases. In this study, we addressed the problem of contactless estimation of RR and classification of RP of one person or two persons in a confined space under realistic conditions. We used three impulse radio ultrawideband (IR-UWB) radars and a 3D depth camera (Kinect) to avoid any blind spot in the room and to ensure that at least one of the radars covers the monitored subjects. This article proposes a subject localization and radar selection algorithm using a Kinect camera to allow the measurement of the respiration of multiple people placed at random locations. Several different experiments were conducted to verify the algorithms proposed in this work. The mean absolute error (MAE) between the estimated RR and reference RR of one-subject and two-subjects RR estimation are 0.61±0.53 breaths/min and 0.68±0.24 breaths/min, respectively. A respiratory pattern classification algorithm combining feature-based random forest classifier and pattern discrimination algorithm was developed to classify different respiration patterns including eupnea, Cheyne-Stokes respiration, Kussmaul respiration and apnea. The overall classification accuracy of 90% was achieved on a test dataset. Finally, a real-time system showing RR and RP classification on a graphical user interface (GUI) was implemented for monitoring two subjects.

Women’s Respiratory Movements during Spontaneous Breathing and Physical Fitness: A Cross-Sectional, Correlational Study

“…The testing of respiratory movements was conducted using respiration belts (Vernier, Beaverton, OR, USA), which is a strap of fabric with a resistive stretch sensor embedded into it. Researchers used one or two belts to test the respiration rate [34], breathing maneuvers (abdominal breathing and chest breathing) [35], or respiratory waveform [36]. In the present study, we used two belts.…”

A low-complexity sleep apnea detection method using millimeter-wave radar

“…According to the latest criteria in the American Academy of Sleep Medicine (AASM) manual, sleep apnea is defined as a decrease in the amplitude of the respiratory signal of more than 90% from the pre-event baseline value with a duration of more than 10 seconds. The existing sleep apnea detection methods generally include polysomnography based, electrocardiography based, 1 oxygen saturation based, 2 audio based, 3 visual based 4 and radar based [5][6][7][8][9][10][11][12][13][14] methods. Among them, the radar sensor has the advantages of completely non-contact, continuously stable performance, privacy protection, and not being affected by the covering of clothes and quilts.…”

“…Therefore, it has been considered as an very important non-contact sleep apnea detection technique. Radar-based sleep apnea detection methods are mainly divided into three categories: traditional detection method (TDM), [5][6][7] machine learning method (MLM), [8][9][10][11][12] and deep learning method (DLM). 13,14 Fugui Qi, et.…”

Monitoring Respiratory Motion during VMAT Treatment Delivery Using Ultra-Wideband Radar

“…reported on the simulation of UWB radar system for distinguishing between costal and abdominal breathing [ 20 , 24 ]. Among the radar systems evaluated, multi-radar systems are preferred due to their increase in positional accuracy using beamforming [ 19 , 20 , 21 , 24 , 25 , 26 ]. Classification of human activities and breathing patterns in presence of interference was addressed for monitoring people in smart homes or in prisons [ 27 ].…”

“…Recently, there have been several works that perform respiratory pattern classification based on radar signals. Abnormal breathing patterns are commonly classified including Biot’s respiration, Cheyne Stokes respiration, Kussmaul breathing and apneas [ 26 ]. Classifiers that were used include support vector machines, random forests and other classifiers [ 29 , 30 ].…”