Respiration Rate Estimation via Smartwatch-based Photoplethysmography and Accelerometer Data: A Transfer Learning Approach

Abstract

Respiration Rate (RR) is a biomarker for several illnesses that can be extracted from biosignals, such as photoplethysmogram (PPG) and accelerometers. Smartwatch-based PPG signals are more prone to noise interference, particularly within their lower frequency spectrum where respiratory data is embedded. Therefore, existing methods are insufficient for extracting RR from PPG data collected from wrists reliably. Additionally, accelerometer sensors embedded in smartwatches capture respiration-induced motion and can be integrated with PPG signals to improve RR extraction. This paper proposes a deep learning-based model to extract RR from raw PPG and accelerometer signals captured via a smartwatch. The proposed network combines dilated residual inception module and Multi-Scale convolutions. We propose a pre-trained foundation model for smartwatch-based RR extraction and apply a transfer learning technique to enhance the generalizability of our method across different datasets. We test the proposed method using two public datasets (i.e., WESAD and PPG-DaLiA). The proposed method shows the Mean Absolute Error (MAE) of 2.29 and 3.09 and Root Mean Squared Errors (RMSE) of 3.11 and 3.79 across PPG-DaLiA and WESAD datasets, respectively. In contrast, the best results obtained by the existing methods are an MAE of 2.68, an RMSE of 3.5 for PPG-DaLiA, an MAE of 3.46, and an RMSE of 4.02 for WESAD datasets.