论文标题
心率估计的多头跨注意力PPG和运动信号融合
Multi-Head Cross-Attentional PPG and Motion Signal Fusion for Heart Rate Estimation
论文作者
论文摘要
如今，炉膛速率（HR）监测是利用光摄影学（PPG）传感器的几乎所有腕上戴的设备的关键特征。但是，ARM运动会影响基于PPG的HR跟踪的性能。通常通过将PPG信号与惯性测量单元产生的数据融合来解决此问题。因此，已经提出了深度学习算法，但是它们被认为太复杂了，无法在可穿戴设备上部署，并且缺乏结果的解释性。在这项工作中，我们提出了一种新的深度学习模型Pulse，该模型利用了时间卷积和多头交叉注意力，以提高传感器融合的有效性，并迈向解释性。我们评估了三个公开可用数据集上的脉冲性能，在最广泛可用的数据集PPG-Dalia上将平均绝对误差降低了7.56％。最后，我们证明了脉搏的解释性以及将注意模块应用于PPG和运动数据的好处。
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.
