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)传感器可以高精度地估计心率(HR)。然而,尽管之前已经有研究致力于将基于PPG传感器的HR估计应用于嵌入式设备中,但这仍然面临着高能耗的高频率PPG采样和资源密集型的机器学习模型的挑战。在本项工作中,我们旨在探索更适合低功耗和资源受限的嵌入式设备的HR估计技术。具体而言,我们寻求设计技术,可在低频率PPG采样、小模型大小和快速推理时间的情况下提供高精度的HR估计。首先,我们展示了通过结合信号处理和机器学习,可以将PPG采样频率从125 Hz降低到仅25 Hz,同时提高HR估计的准确性。这种组合还有助于减少机器学习模型的特征大小,导致模型变得更小。此外,我们对不同的机器学习模型和特征大小进行了全面的分析,比较它们的准确性、模型大小和推理时间。所探究的模型包括决策树(DT)、随机森林(RF)、K近邻(KNN)、支持向量机(SVM)和多层感知器(MLP)。实验使用了广泛使用的数据集和自己收集的数据集。实验结果表明,我们的方法通过结合信号处理和机器学习,使用低频率PPG数据,HR估计的误差仅为5%。此外,我们的分析表明,DT模型具有10到20个输入特征时通常具有较高的准确性,同时模型大小小好几个数量级且推理时间更快。