Remote Blood Pressure Estimation from Facial Videos Using Transfer Learning: Leveraging PPG to rPPG Conversion

Blood pressure (BP) monitoring is crucial for health assessment, but existing contact-based methods face cost and comfort barriers. Remote photoplethysmography (rPPG) offers a promising contactless solution, yet research is hampered by limited rPPG datasets with corresponding BP labels. This paper presents a transfer learning methodology for BP measurement. This approach involves utilizing a base dataset comprising signals produced by PPG (PPG-signals) to acquire knowledge that can be transferred to a target dataset containing signals generated by rP PG (rPPG-signals). In our study, we trained diverse deep-learning models using publicly available datasets containing PPG-signals. Subsequently, these models were fine-tuned and evaluated using a public dataset that specifically consists of rP PG-signals. Additionally, we explored the relationship between BP and heart rate, and examined different loss functions and normalization approaches to optimize the performance of the deep learning models. The findings of our study demonstrate that our best model achieved a better performance than the state-of-the-art model, with mean absolute error (MAE) of 8.721 (reduced by 4.879) mmHg and 8.653 (reduced by 1.647) mmHgfor systolic blood pres-sure (SBP) and diastolic blood pressure (DBP) in a dataset with clinical settings, showing promising potential for remote BP estimation.