Research on heart rate (HR) estimation using wrist-worn photoplethysmography (PPG) sensors have progressed rapidly owing to the prominence of commercial sensing modules, used widely for lifestyle monitoring. Reported methodologies have been fairly successful in mitigating the effect of motion artifacts (MA) in ambulatory environment for HR estimation. Recently, a learning framework, CorNET, employing two-layer convolution neural networks (CNN) and two-layer long short-term network (LSTM) was successfully reported for estimating HR from MA-induced PPG signals. However, such a network topology with large number of parameters presents a challenge, towards low-complexity hardware implementation aimed at on-node processing. In this paper, we demonstrate a fully binarized network (bCorNET) topology and its corresponding algorithm-to-architecture mapping and energy-efficient implementation for HR estimation. The proposed framework achieves a MAE of 6.67 $\pm$ 5.49 bpm when evaluated on 22 IEEE SPC subjects. The design, synthesized with ST65 nm technology library achieving 3 GOPS @ 1 MHz, consumes 56.1 $\mu$ J per window with occupied 1634K NAND2 equivalent cell area and had a latency of 32 ms when estimating HR every 2 s from PPG signals.

中文翻译：

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Binary CorNET：从Wrist-PPG进行HR估算的加速器。

由于腕带式光电容积描记法（PPG）传感器对心率（HR）的估计，研究的迅速发展是由于商业传感模块的广泛应用，广泛用于生活方式监测。报告的方法在缓解动态环境中运动伪影（MA）对HR估计的影响方面相当成功。最近，成功地报告了一种学习框架CorNET，该框架使用两层卷积神经网络（CNN）和两层长短期网络（LSTM）从MA诱导的PPG信号估计HR。但是，这种具有大量参数的网络拓扑结构对面向节点处理的低复杂度硬件实现提出了挑战。在本文中，我们演示了完全二值化的网络（bCorNET）拓扑及其相应的算法到体系结构的映射以及HR估算的节能实现。提议的框架的MAE为6.67$ \ pm $在22个IEEE SPC主题上评估时为5.49 bpm。该设计采用ST65 nm技术库进行综合，在1 MHz时达到3 GOPS，消耗56.1$ \ mu $ 每个窗口J占用1634K NAND2等效单元面积，并且从PPG信号每2 s估计HR时有32 ms的等待时间。