# KID-PPG: Knowledge Informed Deep Learning for Extracting Heart Rate from a Smartwatch

arXiv: https://arxiv.org/abs/2405.09559 · HTML: https://mlsanity.com/paper/2405.09559/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 33 references that are in the map.
- First posted on arXiv 2 May 2024.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?](https://mlsanity.com/paper/1703.04977/) (2017)
- [Informed Machine Learning -- A Taxonomy and Survey of Integrating Knowledge into Learning Systems](https://mlsanity.com/paper/1903.12394/) (2019)
- [Which Shortcut Cues Will DNNs Choose? A Study from the Parameter-Space Perspective](https://mlsanity.com/paper/2110.03095/) (2021)
- [TROIKA: A General Framework for Heart Rate Monitoring Using Wrist-Type Photoplethysmographic Signals During Intensive Physical Exercise](https://mlsanity.com/paper/1409.5181/) (2014)
- [Q-PPG: Energy-Efficient PPG-based Heart Rate Monitoring on Wearable Devices](https://mlsanity.com/paper/2203.14907/) (2022)
- [BeliefPPG: Uncertainty-aware Heart Rate Estimation from PPG signals via Belief Propagation](https://mlsanity.com/paper/2306.07730/) (2023)
- [Robust and Energy-efficient PPG-based Heart-Rate Monitoring](https://mlsanity.com/paper/2203.16339/) (2022)
- [Multi-Head Cross-Attentional PPG and Motion Signal Fusion for Heart Rate Estimation](https://mlsanity.com/paper/2210.11415/) (2022)

## Built on by (most cited first, 7 of 7)

- [EnhancePPG: Improving PPG-based Heart Rate Estimation with Self-Supervision and Augmentation](https://mlsanity.com/paper/2412.17860/) (2024)
- [DC is all you need: describing ReLU from a signal processing standpoint](https://mlsanity.com/paper/2407.16556/) (2024)
- [Time series saliency maps: explaining models across multiple domains](https://mlsanity.com/paper/2505.13100/) (2025)
- [Don't Think It Twice: Exploit Shift Invariance for Efficient Online Streaming Inference of CNNs](https://mlsanity.com/paper/2408.03223/) (2024)
- [Temporal Cardiovascular Dynamics for Improved PPG-Based Heart Rate Estimation](https://mlsanity.com/paper/2510.27297/) (2025)
- [Joint Attention Mechanism Learning to Facilitate Opto-physiological Monitoring during Physical Activity](https://mlsanity.com/paper/2502.09291/) (2025)
- [Physically-Constrained Harmonic Separation for Robust Heart and Respiratory Rate Estimation from Wrist Photoplethysmography](https://mlsanity.com/paper/2606.30156/) (2026)

## Abstract (arXiv)

Accurate extraction of heart rate from photoplethysmography (PPG) signals remains challenging due to motion artifacts and signal degradation. Although deep learning methods trained as a data-driven inference problem offer promising solutions, they often underutilize existing knowledge from the medical and signal processing community. In this paper, we address three shortcomings of deep learning models: motion artifact removal, degradation assessment, and physiologically plausible analysis of the PPG signal. We propose KID-PPG, a knowledge-informed deep learning model that integrates expert knowledge through adaptive linear filtering, deep probabilistic inference, and data augmentation. We evaluate KID-PPG on the PPGDalia dataset, achieving an average mean absolute error of 2.85 beats per minute, surpassing existing reproducible methods. Our results demonstrate a significant performance improvement in heart rate tracking through the incorporation of prior knowledge into deep learning models. This approach shows promise in enhancing various biomedical applications by incorporating existing expert knowledge in deep learning models.