Recent studies showed that Photoplethysmography (PPG) sensors embedded in wearable devices can estimate heart rate (HR) with high accuracy. However, despite of prior research efforts, applying PPG sensor based HR estimation to embedded devices still faces challenges due to the energy-intensive high-frequency PPG sampling and the resource-intensive machine-learning models. In this work, we aim to explore HR estimation techniques that are more suitable for lower-power and resource-constrained embedded devices. More specifically, we seek to design techniques that could provide high-accuracy HR estimation with low-frequency PPG sampling, small model size, and fast inference time. First, we show that by combining signal processing and ML, it is possible to reduce the PPG sampling frequency from 125 Hz to only 25 Hz while providing higher HR estimation accuracy. This combination also helps to reduce the ML model feature size, leading to smaller models. Additionally, we present a comprehensive analysis on different ML models and feature sizes to compare their accuracy, model size, and inference time. The models explored include Decision Tree (DT), Random Forest (RF), K-nearest neighbor (KNN), Support vector machines (SVM), and Multi-layer perceptron (MLP). Experiments were conducted using both a widely-utilized dataset and our self-collected dataset. The experimental results show that our method by combining signal processing and ML had only 5% error for HR estimation using low-frequency PPG data. Moreover, our analysis showed that DT models with 10 to 20 input features usually have good accuracy, while are several magnitude smaller in model sizes and faster in inference time.

翻译：近期研究表明，可穿戴设备中嵌入的光电容积描记法（PPG）传感器能够高精度估计心率。然而，尽管已有诸多研究努力，将基于PPG传感器的心率估计应用于嵌入式设备仍面临挑战，这源于高能耗的高频PPG采样和资源密集型的机器学习模型。本研究旨在探索更适用于低功耗、资源受限嵌入式设备的心率估计技术。具体而言，我们寻求设计能够通过低频PPG采样、小模型尺寸和快速推理时间提供高精度心率估计的技术。首先，我们证明通过结合信号处理与机器学习，可将PPG采样频率从125 Hz降至仅25 Hz，同时提高心率估计精度。这种结合还有助于减小机器学习模型的特征维度，从而获得更小的模型。此外，我们对不同机器学习模型及其特征维度进行了全面分析，对比其精度、模型尺寸和推理时间。所探索的模型包括决策树（DT）、随机森林（RF）、K近邻（KNN）、支持向量机（SVM）和多层感知器（MLP）。实验采用广泛使用的数据集和自采集数据集进行。结果表明，结合信号处理与机器学习的方法在使用低频PPG数据时，心率估计误差仅为5%。此外，我们的分析显示，包含10至20个输入特征的DT模型通常具有良好精度，同时模型尺寸小数个数量级且推理速度更快。