Photoplethysmography (PPG) is a simple optical technique used to detect blood volume changes in the micro-vascular bed of tissue in order to track the heartbeat. Smart-phone PPG, performed with the phones camera, has became popular in recent years due to a boom in digital health apps that help people monitor their health parameters. However, many apps struggle with getting readings that are accurate enough to estimate heart rate variability (HRV) one of the most popular biomarkers in the preventive health space. The main obstacle is the multitude of factors that impact PPG results: unique technical characteristics of different smartphone models, frames per second (FPS) rate and the way color is recoarded, brightness and ambient flash levels, finger placement, in-measurement movement, etc. These factors may decrease the accuracy of the signal extracted from the camera's video stream and produce additional errors in the computation of HRV parameters. Thus, there is a need to estimate signal quality and predict possible bias in HRV parameter calculation. In this paper, we describe the method for processing signal from smartphone cameras, estimating signal quality, recognizing RR intervals, and predicting bias of simple HRV parameters.