论文标题
在体育锻炼期间，使用腕型光摄像学（PPG）信号进行监督心率跟踪，而无需同时加速信号
Supervised Heart Rate Tracking using Wrist-Type Photoplethysmographic (PPG) Signals during Physical Exercise without Simultaneous Acceleration Signals
论文作者
论文摘要
基于PPG的心率（HR）监测最近随着智能手表和智能乐队等可穿戴设备的出现引起了很多关注。但是，由于腕带跌跌撞撞引起的严重运动伪像（MA），在受试者进行大量体育锻炼的情况下，基于PPG的HR监测是一个具有挑战性的问题。这项工作提出了一种基于神经网络（NN）监督学习的新方法。通过在基准数据集[1]上的模拟，我们达到了可接受的估计准确性并提高了与文献相比的运行时间。这项工作的主要贡献是，它减轻了同时加速信号的需求。仿真结果表明，尽管所提出的方法没有处理同时加速信号，但它仍然可以在基准数据集上达到可接受的平均绝对误差（MAE）为1.39 BEATS（BPM）。
PPG based heart rate (HR) monitoring has recently attracted much attention with the advent of wearable devices such as smart watches and smart bands. However, due to severe motion artifacts (MA) caused by wristband stumbles, PPG based HR monitoring is a challenging problem in scenarios where the subject performs intensive physical exercises. This work proposes a novel approach to the problem based on supervised learning by Neural Network (NN). By simulations on the benchmark datasets [1], we achieve acceptable estimation accuracy and improved run time in comparison with the literature. A major contribution of this work is that it alleviates the need to use simultaneous acceleration signals. The simulation results show that although the proposed method does not process the simultaneous acceleration signals, it still achieves the acceptable Mean Absolute Error (MAE) of 1.39 Beats Per Minute (BPM) on the benchmark data set.
