Abstract
Contactless detection is one of the new frontiers of technological innovation in the field of healthcare, enabling unobtrusive measurements of biomedical parameters. Compared to conventional methods for Heart Rate (HR) detection that employ expensive and/or uncomfortable devices, such as the Electrocardiograph (ECG) or pulse oximeter, contactless HR detection offers fast and continuous monitoring of heart activities and provides support for clinical analysis without the need for the user to wear a device. This paper presents a validation study for a contactless HR estimation method exploiting RGB (Red, Green, Blue) data from a Microsoft Kinect v2 device. This method, based on Eulerian Video Magnification (EVM), Photoplethysmography (PPG) and Videoplethysmography (VPG), can achieve performance comparable to classical approaches exploiting wearable systems, under specific test conditions. The output given by a Holter, which represents the gold-standard device used in the test for ECG extraction, is considered as the ground-truth, while a comparison with a commercial smartwatch is also included. The validation process is conducted with two modalities that differ for the availability of a priori knowledge about the subjects’ normal HR. The two test modalities provide different results. In particular, the HR estimation differs from the ground-truth by 2% when the knowledge about the subject’s lifestyle and his/her HR is considered and by 3.4% if no information about the person is taken into account.
Highlights
The measurement and monitoring of vital signs, such as Heart Rate (HR) and blood pressure, are of interest to the healthcare system [1], mainly because they can help with diagnosis and follow-up of different medical issues, including heart diseases
Regarding the approach based on Eulerian Video Magnification (EVM) working on RGB data from Kinect, Table 2 shows results obtained from different Region of Interest (ROI), and T (Total) is the average of all of them
N + F is the mean value of the sum of the N and F ROIs; T is the mean of the sum of all the ROI, identified as Total ROI; SW stands for Smartwatch
Summary
The measurement and monitoring of vital signs, such as Heart Rate (HR) and blood pressure, are of interest to the healthcare system [1], mainly because they can help with diagnosis and follow-up of different medical issues, including heart diseases. HR detection through video processing is mainly based on extremely small variations of skin color, which are not perceptible to the human eye, caused by the flow of blood in the tissues. The aim of the work here presented is to propose a validation process of a contactless HR estimation approach, based on the processing of RGB images captured using Microsoft Kinect v2 [19,20,21,22,23,24] and enhanced by the EVM method, to amplify the changes in skin color due to blood flow. The choice of Kinect is motivated by the fact that it can provide additional information to RGB data, i.e., depth, infrared and skeleton frames This capability can enable other applications, such as the monitoring of the physical activity and HR at the same time.
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