CameraHRV

Overview: A Robust method to extract HRV using a camera

The inter-beat-interval (the time period of the cardiac cycle) changes slightly for every heartbeat; this variation is measured as Heart Rate Variability (HRV). HRV is presumed to occur due to interactions between the parasympathetic and sympathetic nervous system. Therefore, it is sometimes used as an indicator of the stress level of an individual. HRV also reveals some clinical information about cardiac health.

New Dataset – relevant for distancePPG, PulseCam and CameraHRV – see information below

The Problem: 

Currently, HRV is accurately measured using contact devices such as a pulse oximeter. However, recent research in the field of non-contact imaging Photoplethysmography (iPPG) has made vital sign measurements using just the video recording of any exposed skin (such as a person’s face) possible. The current signal processing methods for extracting HRV using peak detection perform well for contact-based systems but have poor performance for the iPPG signals. The main reason for this poor performance is the fact that current methods are sensitive to large noise sources which are often present in iPPG data. Further, current methods are not robust to motion artifacts that are common in iPPG systems.

Main Purpose: Extracting heart rate and heart rate variability(HRV) from imaging Photoplethysmography signals acquired using cameras has been an advancing area of research. Non-contact or remote measurement of HRV has many potential applications such as stress or fatigue monitoring at the workplace as well as early detection of autonomic cardiac neuropathy. As measuring HRV during different activities such as deep breathing and engagement is of interest, we collected a unique dataset that provides researchers the resources to investigate non-contact iPPG based HRV and HR measurements. Since the dataset consists of activities with complex facial movement, researchers can use it to develop iPPG algorithms that are robust to such high facial motion scenarios The dataset consists of facial video recordings of 12 subjects (8 male, six female) under different engagement activities such as reading a webpage, talking about their day, watching a movie trailer and deep breathing. It also contains videos when they were stationary to establish a baseline. The video for each activity is 2 minutes. Ground truth PPG data were collected simultaneously using an FDA approved pulse oximeter. The experiment setup details are: