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.
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.
['Gonzalez Viejo|Claudia|C|', 'Fuentes|Sigfredo|S|0000-0002-0377-5085', 'Torrico|Damir D|DD|', 'Dunshea|Frank R|FR|0000-0003-3998-1240']
D001795:Blood Pressure Determination D000069956:Chocolate D005145:Face D005260:Female D005512:Food Hypersensitivity D006339:Heart Rate D006801:Humans D000069550:Machine Learning D008297:Male D008991:Monitoring, Physiologic D017156:Photoplethysmography D012815:Signal Processing, Computer-Assisted D014741:Video Recording
heart rate blood pressure machine learning modelling computer vision non-intrusive sensors