  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.
