This study presents an open-source remote photoplethysmography (rPPG) method for estimating heart rate from webcam videos, tested on videos collected via an online platform without control over hardware or environmental conditions. The rPPG algorithm was evaluated against two datasets: a validated rPPG dataset (CohFace) and a dataset from a validated mobile heart rate application. Results showed a strong correlation between rPPG-derived heart rate and true heart rate, with minimal differences between the two measures. The algorithm performed well even under suboptimal conditions, such as varying lighting, distance, and motion, demonstrating its potential for use in online experiments. The study involved two experiments. In the first, the rPPG algorithm was tested on the CohFace dataset, which included 164 videos from 40 individuals. The algorithm showed a strong correlation with true heart rate, with a median difference of -0.412 BPM. In the second experiment, 231 online video recordings were analyzed, and the rPPG algorithm was compared to finger pulse oximeter data from a validated mobile application. The results showed a strong correlation between rPPG and the mobile application's heart rate measurements, with a median difference of -2.56 BPM. The rPPG algorithm was found to be accurate even in uncontrolled online environments, making it a promising tool for remote physiological measurement. The study highlights the potential of rPPG for large-scale online psychological and physiological research, enabling the collection of data from diverse populations. However, the study also notes limitations, including variations in accuracy depending on video quality and skin tone. Despite these limitations, the rPPG algorithm proved effective in estimating heart rate from webcam videos, offering a cost-effective and accessible method for remote physiological measurement.This study presents an open-source remote photoplethysmography (rPPG) method for estimating heart rate from webcam videos, tested on videos collected via an online platform without control over hardware or environmental conditions. The rPPG algorithm was evaluated against two datasets: a validated rPPG dataset (CohFace) and a dataset from a validated mobile heart rate application. Results showed a strong correlation between rPPG-derived heart rate and true heart rate, with minimal differences between the two measures. The algorithm performed well even under suboptimal conditions, such as varying lighting, distance, and motion, demonstrating its potential for use in online experiments. The study involved two experiments. In the first, the rPPG algorithm was tested on the CohFace dataset, which included 164 videos from 40 individuals. The algorithm showed a strong correlation with true heart rate, with a median difference of -0.412 BPM. In the second experiment, 231 online video recordings were analyzed, and the rPPG algorithm was compared to finger pulse oximeter data from a validated mobile application. The results showed a strong correlation between rPPG and the mobile application's heart rate measurements, with a median difference of -2.56 BPM. The rPPG algorithm was found to be accurate even in uncontrolled online environments, making it a promising tool for remote physiological measurement. The study highlights the potential of rPPG for large-scale online psychological and physiological research, enabling the collection of data from diverse populations. However, the study also notes limitations, including variations in accuracy depending on video quality and skin tone. Despite these limitations, the rPPG algorithm proved effective in estimating heart rate from webcam videos, offering a cost-effective and accessible method for remote physiological measurement.