Heart Rate Variability using the phone's camera

No doubt the main inconvenience of HRV-based applications is the need for a heart rate monitor. Even when you have one, performing your daily measurement can be a burden (sensor needs to be wet and comfort is clearly an issue). 

Can smartphones come to the rescue? Current generation phones include both a camera and a light emitting diode, which can be used for reflection based bio-optical imaging. 

The technique is called photoplethysmography (PPG for short) and consists in detecting changes in blood volume during a cardiac cycle, by illuminating the skin and measuring changes in light absorption. PPG has become quite a popular non-invasive method for extracting physiological measurements such as heart rate and oxygen saturation. However, most applications today focus simply on heart rate, and it is not clear from literature if HRV features can also be reliably extracted using a phone's camera [1]. 

Here is the good news: it is indeed possible to achieve good accuracy in HRV measurements using this technique, but the methods needed are slightly more complicated than acquiring a video and computing peak detection on the PPG signal (which is sufficient for heart rate measurement). This post covers the steps involved in the implementation of Camera HRV, the iPhone app I developed to measure HRV using the phone's camera. The algorithms are part of HRV4Training since version 3.2.

Later in 2017, we have also validated these methods and published the results with respect to ECG and Polar straps, showing equivalency. You can find the full paper here.

Overview

1 - Data acquisition from the phone's camera
2 - Filtering & smoothing
3 - Resampling with cubic spline interpolation
4 - Peak detection
5 - Artifact removal and features extraction
6 - Comparison with heart rate monitors (Polar H7)
7 - Tips

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