Heart rate data are collected often in human factors studies. Advances in open hardware platforms and offtheshelf photoplethysmogram (PPG) sensors allow the nonintrusive collection of heart rate data at very low cost. However, the signal is not trivial to analyse, since the morph
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Heart rate data are collected often in human factors studies. Advances in open hardware platforms and offtheshelf photoplethysmogram (PPG) sensors allow the nonintrusive 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. PPG is often preferable because it can be collected less intrusively. However, few validated open source available algorithms exist that handle PPG data well, as most of these algorithms are specifically designed for ECG data. We have developed a novel algorithm specifically for PPG data collected in noisy fieldor simulatorbased settings. The main aim of this paper is to present the validation of a novel algorithm on a PPG dataset collected in a recent driving simulator experiment. The dataset was manually annotated, and performance of the algorithm compared to two other popular open source available algorithms. We show that the algorithm performs well and displays superior performance on the PPG dataset. Implications and further steps are discussed. @en
