Assessing an Automatic Procedure of Extraction of Physiological Parameters from Skin using Video Photoplethysmography.

Video Photoplethysmography (vPPG) allows for estimation of blood volume pulse (BVP) from the skin by means of a video camera recording at high frequency rate. The estimation procedure presents several drawbacks in its application to real world conditions, such as light changes or movements that often generate artifacts in the extracted BVP waveform. In addition, the process requires a skin segmentation algorithm to distinguish skin pixels from the background. To date, even the most refined skin segmentation algorithms still need a manual definition that could lead to incorrect pixel classification, and consequently to a decrease in the signal-to-noise ratio (SNR). We here propose a fully autonomic procedure able to extract BVP from video recordings of the skin in real world conditions.The experimental protocol is designed to record the signals of interest and to evaluate changes in the Autonomic Nervous System modulation of the heart during a baseline condition and a controlled breathing phase. Video recordings are gathered from 4 young healthy subjects (age: 21±1 years). vPPG signals are processed in order to extract the BVP waveform, and a peak detection algorithm detects pulse wave peaks that are then used to compute specific measures of heart rate variability (HRV).The procedure is successfully validated by comparing the extracted HRV measures against those extracted using a finger photoplethysmograph (fPPG) using three different skin segmentation algorithms from BVP signals.

Identification and Tracking of Physiological Parameters from Skin using Video Photoplethysmography.

In recent years, there has been a growing interest in video Photoplethysmography (vPPG), a technique able to estimate cardiovascular parameters from video recordings of the skin. Despite the growing interest in vPPG technology, there are still problems in extracting the correct waveform of blood volume pulse, mainly due to real world artifacts, such as changes in light condition and movement artifacts. Another important issue is the correct definition of skin against background. Therefore, we propose an algorithm of skin detection that is able to recognize skin pixels solid to variations of luminosity. We recorded the signals of interest during an experimental protocol designed to provide thermal stimulation and observe the resulting Autonomic Nervous System changes. Experimental data were gathered from 10 young healthy subjects (age: 21±2 years). Video recordings are processed using a band-pass filter and then an automatic algorithm of peak detection is applied to detect the pulse wave peaks, then used to estimate heart rate variability (HRV). The efficiency and stability of the algorithm are compared against finger-PPG waveforms. Preliminary results show an overall statistical agreement between time and frequency domain indexes. However, further efforts are required to improve the estimation of frequency components, particularly during rest.