Remote photoplethysmography (rPPG) is a non-invasive technique that allows heart rate (HR) estimation from facial videos, which is especially useful in contexts where physical contact should be minimized. This work presents a preliminary evaluation of the accuracy of a specific configuration of the pyVHR framework, based on the POS (Plane Orthogonal to Skin) method, for estimating HR from RGB signals extracted from video. The obtained signal was compared to a reference electrocardiogram (ECG) signal in lead DII, recorded simultaneously in three subjects. Bland-Altman analysis was applied to assess agreement between the two methods. Given the very small sample, findings are preliminary. Although limitations related to motion sensitivity and minimal preprocessing were identified, the results suggest that the selected configuration can track HR with reasonable accuracy under controlled conditions. As an additional contribution, we developed a computer software with a graphical interface implementing this configuration to make it accessible to users without programming knowledge. Moreover, this study contributes to the field of bioengineering by integrating signal processing techniques to enhance both accessibility and accuracy in physiological monitoring.