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This paper describes the functioning and development of HeartPy: a heart rate analysis toolkit designed for photoplethysmogram (PPG) data. Most openly available algorithms focus on electrocardiogram (ECG) data, which has very different signal properties and morphology, creating a problem with analysis. ECG-based algorithms generally don’t function well on PPG data, especially noisy PPG data collected in experimental studies. To counter this, we developed HeartPy to be a noise-resistant algorithm that handles PPG data well. It has been implemented in Python and C. Arduino IDE sketches for popular boards (Arduino, Teensy) are available to enable data collection as well. This provides both pc-based and wearable implementations of the software, which allows rapid reuse by researchers looking for a validated heart rate analysis toolkit for use in human factors studies.

Analysing Noisy Driver Physiology Real-Time Using Off-the-Shelf Sensors: Heart Rate Analysis Software from the Taking the Fast Lane Project