Real-time vision based respiration monitoring system

Tan, K. S.; Saatchi, Reza; Elphick, Heather; Burke, Derek

doi:10.1109/csndsp16145.2010.5580316

Cited by 81 publications

(51 citation statements)

References 8 publications

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“…Тому останнім часом є тенденція створення та розвитку безконтактних систем моніторингу, які здатні оцінювати показники життєдіяльності пацієнта без безпосереднього контакту з тілом, а відповіднобез створення дискомфорту та обмеження руху. В роботах [6], [7] та [8] описуються методи безконтактного моніторингу параметрів дихання. В цих працях основним параметром, який відслідковується під час моніторингу, є частота дихання.…”

Section: вступunclassified

Non-contact monitoring of ventilation function parameters using optical flow

Bodilovskyi¹

2017

Mìkrosist. elektron. akust.

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Безконтактний моніторинг параметрів вентиляційної функції легень за допомогою методу оптичного потоку Боділовський О. К., ORCID 0000-0003-3993-0750 e-mail bodilowsky@ukr.net Національний технічний університет України "Київський політехнічний інститут імені Ігоря Сікорського" kpi.ua Київ, Україна Реферат-В статті запропоновано метод, що дозволяє отримати розширений набір параметрів вентиляційної функції легень при безконтактному моніторингу за допомогою відеокамери, зокрема тривалості вдиху та видиху під час аналізу потокового відео з верхньої частини тулуба людини. Дані параметри надають додаткові можливості лікарям для діагностики стану пацієнта під час безконтактного моніторингу. Розроблений метод було апробовано на відеозаписах з п'яти здорових добровольців. Значення середньої відносної похибки склали 9.7 відсотка для тривалості вдиху та 18.3 відсотка для тривалості видиху.Бібл. 11, рис. 4, табл. 1. Ключові словапараметри дихання; оптичний потік; тривалість вдиху; тривалість видиху; моніторинг дихання за допомогою камери.

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Section: вступunclassified

Non-contact monitoring of ventilation function parameters using optical flow

Bodilovskyi¹

2017

Mìkrosist. elektron. akust.

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“…Among contactless monitoring systems, properly designed video-processing algorithms are of significant interest. In [7,8,9], contactless monitoring systems are proposed: the first system is embedded in a board with multiple cameras [7], the second one analyzes respiratory movements, but does not include automatic RR estimation [8] and the last one makes use of infrared cameras [9]. Some recent innovative video-based systems for RR measurement and apnea detection are based on advanced video-processing algorithms to enhance small breathing motion, improve apnea event detection, and refine RR estimation [10,11].…”

Section: Introductionmentioning

confidence: 99%

Markov chain modeling and simulation of breathing patterns

Alinovi

Ferrari

Pisani

et al. 2017

Biomedical Signal Processing and Control

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The lack of large video databases obtained from real patients with respiratory disorders makes the design and optimization of video-based monitoring systems quite critical. The purpose of this study is the development of suitable models and simulators of breathing behaviors and disorders, such as respiratory pauses and apneas, in order to allow efficient design and test of video-based monitoring systems. More precisely, a novel Continuous-Time Markov Chain (CTMC) statistical model of breathing patterns is presented. The Respiratory Rate (RR) pattern, estimated by measured vital signs of hospital-monitored patients, is approximated as a CTMC, whose states and parameters are selected through an appropriate statistical analysis. Then, two simulators, software-and hardware-based, are proposed. After validation of the CTMC model, the proposed simulators are tested with previously developed video-based algorithms for the estimation of the RR and the detection of apnea events. Examples of application to assess the performance of systems for video-based RR estimation and apnea detection are presented. The results, in terms of Kullback-Leibler divergence, show that realistic breathing patterns, including specific respiratory disorders, can be accurately described by the proposed model; moreover, the simulators are able to reproduce practical breathing patterns for video analysis. The presented CTMC statistical model can be strategic to describe realistic breathing patterns and devise simulators useful to develop and test novel and effective video processing-based monitoring systems.

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“…A similar system was presented in [13] which used image and signal processing techniques to extract chest and abdominal movement information. The only difference with this system was that it extracted the movement information from a sequence of video images recorded using a single camera.…”

Section: Respiratory Signals and Motionmentioning

confidence: 99%

“…The only difference with this system was that it extracted the movement information from a sequence of video images recorded using a single camera. Subjects were recorded (different shirt colours and patterns were worn to determine if there was any effect on the accuracy) producing sequential images which were stacked, and a specially designed algorithm was developed to perform image subtraction between the current and previous images [13].…”

Section: Respiratory Signals and Motionmentioning

confidence: 99%

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Monitoring Breathing Using a Doppler Radar

Tworzydlo¹

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Correctional institutions are looking to adopt a non-obtrusive method for monitoring the vital signs of inmates in Canada. Doppler radar has been previously investigated as a potential method of vital signs detection and monitoring. This thesis presents an algorithm which uses the baseband output signal received by a Doppler radar to estimate the breathing rate from the detected motion associated with breathing. A reliability measure is provided with breathing rate estimates through the analysis of various signal quality indices. Results demonstrate that the algorithm is able to estimate the subject's breathing rate (with high reliability) when the subject is motionless and breathing normally (mean error of 6 breaths per minute). The algorithm cannot accurately estimate the breathing rate when the subject is moving or holding their breath (mean error of 21 breaths per minute). During breath holds, it was found that the algorithm can likely estimate the subject's heart rate.iii

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Real-time vision based respiration monitoring system

Cited by 81 publications

References 8 publications

Non-contact monitoring of ventilation function parameters using optical flow

Non-contact monitoring of ventilation function parameters using optical flow

Markov chain modeling and simulation of breathing patterns

Monitoring Breathing Using a Doppler Radar

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