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.

本文探讨了低功耗和资源受限的嵌入式设备中更适用于心率估计技术。作者们将信号处理和机器学习相结合，将 PPG 采样频率从 125Hz 降低到 25Hz 以提高准确性，缩小模型大小并加快推理时间。实验结果表明，DT 模型使用 10 至 20 个输入特征通常具有良好的准确性，并几个数量级地降低了模型大小和推理时间。