Comparison of Two Low-Power Signal Processing Algorithms for Optical Heart Rate Monitoring

Photoplethysmographic (PPG) heart rate monitoring is widely used in commercial wearable fitness trackers, including those from Fitbit, Apple, Samsung, etc. Wearable sensors must provide accurate data while consuming minimal power to maximize battery life. In PPG monitors, signal processing is used to convert pulsations in a reflected optical signal to a heart rate measurement given as a beat-to-beat interval. This paper compares two low-power, point-by-point signal processing algorithms suitable for PPG monitoring. One is based on derivative filtering, and the other is based on high pass filtering. Both methods have low computational requirements, but the derivative method is shown to be more robust in tracking heart rate during activity and at rest. It provides near beat-to-beat accuracy in a wearable PPG during walking and biking.