Multi-Head Cross-Attentional PPG and Motion Signal Fusion for Heart Rate Estimation
This work addresses heart rate monitoring for users of wearable devices, offering an incremental improvement in accuracy and explainability.
The paper tackles the problem of heart rate estimation from wrist-worn devices by addressing performance degradation due to arm movements, presenting a new deep learning model called PULSE that reduces mean absolute error by 7.56% on the PPG-DaLiA dataset.
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.