Literature DB >> 28447860

Remote sensing of physiological signs using a machine vision system.

Ali Al-Naji1,2, Kim Gibson3, Javaan Chahl1,4.   

Abstract

The aim of this work is to remotely measure heart rate (HR) and respiratory rate (RR) using a video camera from long range (> 50 m). The proposed system is based on imperceptible signals produced from blood circulation, including skin colour variations and head motion. As these signals are not visible to the naked eye and to preserve the signal strength in the video, we used an improved video magnification technique to enhance these invisible signals and detect the physiological activity within the subject. The software of the proposed system was built in a graphic user interface (GUI) environment to easily select a magnification system to use (colour or motion magnification) and measure the physiological signs independently. The measurements were performed on a set of 10 healthy subjects equipped with a finger pulse oximeter and respiratory belt transducer that were used as reference methods. The experimental results were statistically analysed by using the Bland-Altman method, Pearson's correlation coefficient, Spearman correlation coefficient, mean absolute error, and root mean squared error. The proposed system achieved high correlation even in the presence of movement artefacts, different skin tones, lighting conditions and distance from the camera. With acceptable performance and low computational complexity, the proposed system is a suitable candidate for homecare applications, security applications and mobile health devices.

Keywords:  Long-distance monitoring; colour magnification technique; face detection; graphic user interface; imaging photoplethysmography; motion magnification technique

Mesh:

Year:  2017        PMID: 28447860     DOI: 10.1080/03091902.2017.1313326

Source DB:  PubMed          Journal:  J Med Eng Technol        ISSN: 0309-1902


  4 in total

1.  Simultaneous Tracking of Cardiorespiratory Signals for Multiple Persons Using a Machine Vision System With Noise Artifact Removal.

Authors:  Ali Al-Naji; Javaan Chahl
Journal:  IEEE J Transl Eng Health Med       Date:  2017-09-29       Impact factor: 3.316

2.  Noncontact Heart and Respiratory Rate Monitoring of Preterm Infants Based on a Computer Vision System: Protocol for a Method Comparison Study.

Authors:  Kim Gibson; Ali Al-Naji; Julie-Anne Fleet; Mary Steen; Javaan Chahl; Jasmine Huynh; Scott Morris
Journal:  JMIR Res Protoc       Date:  2019-08-29

3.  Non-Contact Heart Rate and Blood Pressure Estimations from Video Analysis and Machine Learning Modelling Applied to Food Sensory Responses: A Case Study for Chocolate.

Authors:  Claudia Gonzalez Viejo; Sigfredo Fuentes; Damir D Torrico; Frank R Dunshea
Journal:  Sensors (Basel)       Date:  2018-06-03       Impact factor: 3.576

4.  A Pilot Study for Estimating the Cardiopulmonary Signals of Diverse Exotic Animals Using a Digital Camera.

Authors:  Ali Al-Naji; Yiting Tao; Ian Smith; Javaan Chahl
Journal:  Sensors (Basel)       Date:  2019-12-10       Impact factor: 3.576

  4 in total

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