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

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 develo...

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Bibliographic Details
Main Authors: Claudia Gonzalez Viejo, Sigfredo Fuentes, Damir D. Torrico, Frank R. Dunshea
Format: Article
Language:English
Published: MDPI AG 2018-06-01
Series:Sensors
Subjects:
blood pressure
computer vision
heart rate
machine learning modelling
non-intrusive sensors
Online Access:http://www.mdpi.com/1424-8220/18/6/1802

Internet

http://www.mdpi.com/1424-8220/18/6/1802

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