Heart rate (HR) monitoring using photoplethysmography (PPG) is a promising feature in modern wearable devices. PPG is easily contaminated by motion artifacts (MA), hindering estimation of HR. For quasi-periodic motions, previous works generally focused on a few specific motions, such as walking and fast running. However, they may not work well for many different quasi-periodic motions where MA are very complex. In this paper, a robust HR monitoring scheme for different quasi-periodic motions using wrist-type PPG is proposed, which consists of dictionary learning for signal characteristics learning, human motion recognition for the current motion recognition and dictionary selection, sparse representation-based MA elimination for denoising, and spectral peak tracking for HR-related spectral peak tracking. The proposed scheme is robust to MA caused by different motions and has high accuracy. Experiments on six common quasi-periodic motions showed that the average absolute error of heart rate estimation was 2.40 beat per minute, and also showed that the proposed method is more robust than some state-of-the-art approaches for different motions.

中文翻译：

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通过腕式PPG信号对准周期运动进行稳健的心率监测

使用光电容积描记法（PPG）进行心率（HR）监测是现代可穿戴设备中的一项有前途的功能。PPG容易被运动伪影（MA）污染，从而妨碍了HR的估算。对于准周期运动，以前的工作通常集中在一些特定的运动上，例如步行和快速奔跑。但是，它们可能不适用于MA非常复杂的许多不同的准周期运动。本文提出了一种基于腕式PPG的针对不同准周期运动的鲁棒HR监测方案，该方案包括字典学习，信号特征学习，人体运动识别，当前运动识别和字典选择，基于稀疏表示的MA。消除噪声以消除噪声，而频谱峰值跟踪则用于与HR相关的频谱峰值跟踪。所提出的方案对于由不同运动引起的MA是鲁棒的，并且具有高精度。对六个常见的准周期性运动进行的实验表明，心率估计的平均绝对误差为每分钟2.40次，并且还表明，对于不同的运动，该方法比某些最新方法更可靠。