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Open AccessArticle

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

School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Parkville, VIC 3010, Australia
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Sensors 2018, 18(6), 1802; https://doi.org/10.3390/s18061802
Received: 8 May 2018 / Revised: 27 May 2018 / Accepted: 27 May 2018 / Published: 3 June 2018
(This article belongs to the Section Biosensors)
Traditional methods to assess heart rate (HR) and blood pressure (BP) are intrusive and can affect results in sensory analysis of food as participants are aware of the sensors. This paper aims to validate a non-contact method to measure HR using the photoplethysmography (PPG) technique and to develop models to predict the real HR and BP based on raw video analysis (RVA) with an example application in chocolate consumption using machine learning (ML). The RVA used a computer vision algorithm based on luminosity changes on the different RGB color channels using three face-regions (forehead and both cheeks). To validate the proposed method and ML models, a home oscillometric monitor and a finger sensor were used. Results showed high correlations with the G color channel (R2 = 0.83). Two ML models were developed using three face-regions: (i) Model 1 to predict HR and BP using the RVA outputs with R = 0.85 and (ii) Model 2 based on time-series prediction with HR, magnitude and luminosity from RVA inputs to HR values every second with R = 0.97. An application for the sensory analysis of chocolate showed significant correlations between changes in HR and BP with chocolate hardness and purchase intention. View Full-Text
Keywords: blood pressure; computer vision; heart rate; machine learning modelling; non-intrusive sensors blood pressure; computer vision; heart rate; machine learning modelling; non-intrusive sensors
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MDPI and ACS Style

Gonzalez Viejo, C.; Fuentes, S.; Torrico, D.D.; Dunshea, F.R. 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. Sensors 2018, 18, 1802. https://doi.org/10.3390/s18061802

AMA Style

Gonzalez Viejo C, Fuentes S, Torrico DD, Dunshea FR. 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. Sensors. 2018; 18(6):1802. https://doi.org/10.3390/s18061802

Chicago/Turabian Style

Gonzalez Viejo, Claudia; Fuentes, Sigfredo; Torrico, Damir D.; Dunshea, Frank R. 2018. "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" Sensors 18, no. 6: 1802. https://doi.org/10.3390/s18061802

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