IDTL-rPPG: Remote heart rate estimation using instance-based deep transfer learning

Abstract

Remote photoplethysmography (rPPG) holds significant promise for estimating physiological parameters due to its non-contact nature and cost-effectiveness. However, the blood volume pulse (BVP) signal extracted by rPPG is susceptible to motion and artificial noise, leading to challenges in obtaining accurate results. Existing noise-robust deep learning models exhibit limited generalization ability across different environments. To address this issue, we propose an instance-based deep transfer learning for remote heart rate (HR) estimation (IDTL-rPPG), which leverages the source domain model to estimate HR in target domains. IDTL-rPPG comprises a convolutional–deconvolutional network for assessing the quality of BVP signals and a deep learning model for remotely estimating HR. To reduce the gap between the source and target domain, IDTL-rPPG adjusts the weight values of different quality samples in the source domain and applies these weights during the training of the HR estimation model using stochastic gradient descent. We evaluate IDTL-rPPG by constructing four HR estimation deep learning models with commonly used modules, including convolutional layers, residual blocks, long short-term memory (LSTM) layers, and attention modules. The experiments demonstrate that IDTL-rPPG significantly improves the precision of multiple deep learning models in cross-subject experiments and inter dataset experiments.