Embedding Temporal Convolutional Networks for Energy-Efficient PPG-Based Heart Rate Monitoring

TL;DR

This paper introduces a lightweight, robust deep learning approach using Temporal Convolutional Networks and Neural Architecture Search for accurate, energy-efficient heart rate monitoring from PPG signals, even with motion artifacts.

Contribution

It proposes a novel combination of NAS-optimized TCNs and an adaptive estimator selection algorithm for improved PPG-based HR estimation.

Findings

Achieved up to 3.84 BPM MAE on PPGDalia dataset.

Outperformed previous state-of-the-art in HR estimation accuracy.

Deployed models successfully on a low-power microcontroller.

Abstract

Photoplethysmography (PPG) sensors allow for non-invasive and comfortable heart-rate (HR) monitoring, suitable for compact wrist-worn devices. Unfortunately, Motion Artifacts (MAs) severely impact the monitoring accuracy, causing high variability in the skin-to-sensor interface. Several data fusion techniques have been introduced to cope with this problem, based on combining PPG signals with inertial sensor data. Until know, both commercial and reasearch solutions are computationally efficient but not very robust, or strongly dependent on hand-tuned parameters, which leads to poor generalization performance. % In this work, we tackle these limitations by proposing a computationally lightweight yet robust deep learning-based approach for PPG-based HR estimation. Specifically, we derive a diverse set of Temporal Convolutional Networks (TCN) for HR estimation, leveraging Neural Architecture Search (NAS). Moreover, we also introduce ActPPG, an adaptive algorithm that selects among multiple HR estimators depending on the amount of MAs, to improve energy efficiency. We validate our approaches on two benchmark datasets, achieving as low as 3.84 Beats per Minute (BPM) of Mean Absolute Error (MAE) on PPGDalia, which outperforms the previous state-of-the-art. Moreover, we deploy our models on a low-power commercial microcontroller (STM32L4), obtaining a rich set of Pareto optimal solutions in the complexity vs. accuracy space.