Utilizing Time-Distributed Layers to Estimate Vital Parameter from Video Sequences

Imaging photoplethysmography (iPPG) has emerged as an alternative, contactless solution for the monitoring of physiological parameters. Applying iPPG in real-life scenarios, particularly in dynamic illumination with subject movement, presents a significantly challenging task. This paper introduces an iPPG-based non-invasive monitoring method for estimating heart rate while maintaining the privacy of the patient. The deep architecture consists of time-distributed convolutional neural networks and long short-term memory layers, trained on the PURE dataset. All images undergo a series of pre-processing steps, and the segmented region of interest is then provided as input to the deep architecture. The proposed methodology is evaluated using Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), achieving an RMSE of 3.52 and an MAE of 3.79 on test data derived from the PURE dataset, demonstrating comparable performance with other state-of-the-art algorithms. Furthermore, we evaluated the performance on video sequences acquired under dynamic conditions.