Profiling the propagation of error from PPG to HRV features in consumer wearable devices
In this white paper we explore the reliability of the heart rate sensor in the Microsoft Band v2, a wrist worn consumer wearable device, and the propagation of error from heart rate to heart rate variability features. We show that motion artifacts account for most of the error, and that is possible to filter unreliable portions of heart rate data, using the accelerometer sensor present in the wearable device.
We show that the noise has strong frequency components in the HF band, making the SVI HRV feature unreliable.
We also show that long windows are not needed to extract accurate estimations of time domain HRV features.