Heart rate data are often collected in human factors studies, including those into vehicle automation. Advances in open hardware platforms and off-the-shelf photoplethysmogram (PPG) sensors allow the non-intrusive collection of heart rate data at very low cost. However, the signal is not trivial to analyse, since the morphology of PPG waveforms differs from electrocardiogram (ECG) waveforms and shows different noise patterns. Few validated open source available algorithms exist that handle PPG data well, as most of these algorithms are specifically designed for ECG data.
In this paper we present the validation of a novel algorithm named HeartPy, useful for the analysis of heart rate data collected in noisy settings, such as when driving a car or when in a simulator. We benchmark the performance on two types of datasets and show that the developed algorithm performs well. Further research steps are discussed.