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    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

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    12
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    Author
    Viejo, CG; Fuentes, S; Torrico, DD; Dunshea, FR
    Date
    2018-06-01
    Source Title
    Sensors
    Publisher
    MDPI
    University of Melbourne Author/s
    Torrico, Damir; Fuentes Jara, Sigfredo Augusto; Dunshea, Frank; Gonzalez Viejo Duran, Claudia; Gonzalez Viejo Duran, Claudia
    Affiliation
    Agriculture and Food Systems
    Metadata
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    Document Type
    Journal Article
    Citations
    Viejo, C. G., Fuentes, S., Torrico, D. D. & Dunshea, F. 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 (6), https://doi.org/10.3390/s18061802.
    Access Status
    Open Access
    URI
    http://hdl.handle.net/11343/255272
    DOI
    10.3390/s18061802
    Abstract
    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.

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