A non-contact video analysis method using machine learning accurately predicted heart rate and blood pressure compared to traditional sensors, achieving correlations up to R = 0.97.
Observational
Can non-contact video analysis and machine learning accurately estimate heart rate and blood pressure compared to traditional sensors during food sensory evaluation?
Non-contact video analysis using machine learning provides accurate heart rate and blood pressure estimations, offering a non-intrusive alternative for sensory research.
Effect estimate: R 0.97
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.
Viejo et al. (Sun,) reported a observational. Non-contact raw video analysis (RVA) and machine learning vs. Home oscillometric monitor and finger sensor was evaluated on Correlation of heart rate and blood pressure predictions with reference sensors (R 0.97). A non-contact video analysis method using machine learning accurately predicted heart rate and blood pressure compared to traditional sensors, achieving correlations up to R = 0.97.