EnhancePPG: Improving PPG-based Heart Rate Estimation with Self-Supervision and Augmentation
Luca Benfenati, Sofia Belloni, Alessio Burrello, Panagiotis Kasnesis,, Xiaying Wang, Luca Benini, Massimo Poncino, Enrico Macii, Daniele Jahier, Pagliari

TL;DR
EnhancePPG introduces a self-supervised learning and data augmentation framework that significantly improves heart rate estimation accuracy from PPG signals in wearable devices without increasing inference latency.
Contribution
The paper proposes a novel self-supervised pre-training method combined with data augmentation to enhance PPG-based HR estimation models, requiring no additional labeled data.
Findings
Achieved a 12.2% reduction in HR estimation error on PPG-DaLiA dataset.
Improved state-of-the-art HR estimation accuracy from 4.03 BPM to 3.54 BPM.
Method maintains low inference latency suitable for wearable devices.
Abstract
Heart rate (HR) estimation from photoplethysmography (PPG) signals is a key feature of modern wearable devices for health and wellness monitoring. While deep learning models show promise, their performance relies on the availability of large datasets. We present EnhancePPG, a method that enhances state-of-the-art models by integrating self-supervised learning with data augmentation (DA). Our approach combines self-supervised pre-training with DA, allowing the model to learn more generalizable features, without needing more labelled data. Inspired by a U-Net-like autoencoder architecture, we utilize unsupervised PPG signal reconstruction, taking advantage of large amounts of unlabeled data during the pre-training phase combined with data augmentation, to improve state-of-the-art models' performance. Thanks to our approach and minimal modification to the state-of-the-art model, we improve…
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Taxonomy
TopicsNon-Invasive Vital Sign Monitoring · Heart Rate Variability and Autonomic Control
