论文标题
稳健且节能的基于PPG的心率监测
Robust and Energy-efficient PPG-based Heart-Rate Monitoring
论文作者
论文摘要
磨损腕部的PPG传感器，加上轻质算法可以在MCU上运行以实现非侵入性和舒适的监测，但是在运动伪像的情况下，确保基于PPG的强大的PPG心率监测仍然是一个开放的挑战。最近的最新算法结合了PPG和惯性信号，以减轻运动伪像的影响。但是，这些方法的普遍性有限。此外，尚未研究他们在基于MCU的边缘节点上的部署。在这项工作中，我们通过提出使用硬件友好的时间卷积网络（TCN）来解决基于PPG的心脏估计来解决这两个问题。从单个“种子” TCN开始，我们利用自动神经体系结构搜索（NAS）方法来推导丰富的模型家族。其中，我们获得了一个TCN，该TCN在可用的最大PPG数据集（PPGDALIA）上胜过先前的最先进的TCN，其平均绝对误差（MAE）仅为每分钟3.84次（BPM）。此外，我们还测试了一组较小但仍然准确的（MAE为5.64-6.29 bpm）网络，该网络可以部署在商业MCU（STM32L4）上，该网络（STM32L4）所需的5K参数少于5K参数，并且每次推断中仅消耗0.21 MJ的潜伏期。
A wrist-worn PPG sensor coupled with a lightweight algorithm can run on a MCU to enable non-invasive and comfortable monitoring, but ensuring robust PPG-based heart-rate monitoring in the presence of motion artifacts is still an open challenge. Recent state-of-the-art algorithms combine PPG and inertial signals to mitigate the effect of motion artifacts. However, these approaches suffer from limited generality. Moreover, their deployment on MCU-based edge nodes has not been investigated. In this work, we tackle both the aforementioned problems by proposing the use of hardware-friendly Temporal Convolutional Networks (TCN) for PPG-based heart estimation. Starting from a single "seed" TCN, we leverage an automatic Neural Architecture Search (NAS) approach to derive a rich family of models. Among them, we obtain a TCN that outperforms the previous state-of-the-art on the largest PPG dataset available (PPGDalia), achieving a Mean Absolute Error (MAE) of just 3.84 Beats Per Minute (BPM). Furthermore, we tested also a set of smaller yet still accurate (MAE of 5.64 - 6.29 BPM) networks that can be deployed on a commercial MCU (STM32L4) which require as few as 5k parameters and reach a latency of 17.1 ms consuming just 0.21 mJ per inference.
