Multi-Head Cross-Attentional PPG and Motion Signal Fusion for Heart Rate Estimation

TL;DR

This paper introduces PULSE, a deep learning model using multi-head cross-attention for improved fusion of PPG and motion signals to estimate heart rate, enhancing accuracy and explainability on public datasets.

Contribution

The paper proposes PULSE, a novel deep learning architecture with temporal convolutions and cross-attention, advancing sensor fusion and interpretability in wearable heart rate monitoring.

Findings

Reduced mean absolute error by 7.56% on PPG-DaLiA dataset

Demonstrated improved sensor fusion effectiveness

Showed benefits of attention modules for explainability

Abstract

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 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.