Feature-Level Cross-Attentional PPG and Motion Signal Fusion for Heart Rate Estimation

Nowadays, Hearth Rate (HR) monitoring is a key feature of almost all wrist-worn devices exploiting photoplethysmography (PPG) sensors. However, arm movements affect the performance of PPG-based HR tracking. This issue is usually addressed by fusing the PPG signal with data produced by inertial measurement units. Thus, deep learning algorithms have been proposed, but they are considered too complex to deploy on wearable devices and lack the explainability of results. In this work, we present a new deep learning model, PULSE, which exploits temporal convolutions and feature-level multi-head cross-attention to improve sensor fusion's effectiveness and achieve a step towards explainability. We evaluate the performance of PULSE on three publicly available datasets, reducing the mean absolute error by 7.56% on the most extensive available dataset, PPG-DaLiA. Finally, we demonstrate the explainability of PULSE and the benefits of applying attention modules to PPG and motion data.

Automated summary

This paper introduces a new deep learning model called PULSE, which utilizes temporal convolutions and feature-level multi-head cross-attention to improve the effectiveness of sensor fusion and achieve a certain level of explainability. Through evaluation on publicly available datasets, PULSE reduces the mean absolute error by 7.56% on the most extensive dataset, PPG-DaLiA. Additionally, the paper demonstrates the benefits of applying attention modules to PPG and motion data.