# WildPPG: A Real-World PPG Dataset of Long Continuous Recordings

arXiv: https://arxiv.org/abs/2412.17540 · HTML: https://mlsanity.com/paper/2412.17540/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 74 references that are in the map.
- First posted on arXiv 23 Dec 2024.

## Builds on (most cited first, 18 of 18)

- [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)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Barlow Twins: Self-Supervised Learning via Redundancy Reduction](https://mlsanity.com/paper/2103.03230/) (2021)
- [TS2Vec: Towards Universal Representation of Time Series](https://mlsanity.com/paper/2106.10466/) (2021)
- [Time-Series Representation Learning via Temporal and Contextual Contrasting](https://mlsanity.com/paper/2106.14112/) (2021)
- [With a Little Help from My Friends: Nearest-Neighbor Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2104.14548/) (2021)
- [HOLMES: Health OnLine Model Ensemble Serving for Deep Learning Models in Intensive Care Units](https://mlsanity.com/paper/2008.04063/) (2020)
- [TROIKA: A General Framework for Heart Rate Monitoring Using Wrist-Type Photoplethysmographic Signals During Intensive Physical Exercise](https://mlsanity.com/paper/1409.5181/) (2014)
- [What Makes Good Contrastive Learning on Small-Scale Wearable-based Tasks?](https://mlsanity.com/paper/2202.05998/) (2022)
- [CardioGAN: Attentive Generative Adversarial Network with Dual Discriminators for Synthesis of ECG from PPG](https://mlsanity.com/paper/2010.00104/) (2020)
- [Finding Order in Chaos: A Novel Data Augmentation Method for Time Series in Contrastive Learning](https://mlsanity.com/paper/2309.13439/) (2023)
- [Photoplethysmography-Based Heart Rate Monitoring in Physical Activities via Joint Sparse Spectrum Reconstruction](https://mlsanity.com/paper/1503.00688/) (2015)
- [PPGnet: Deep Network for Device Independent Heart Rate Estimation from Photoplethysmogram](https://mlsanity.com/paper/1903.08912/) (2019)
- [BeliefPPG: Uncertainty-aware Heart Rate Estimation from PPG signals via Belief Propagation](https://mlsanity.com/paper/2306.07730/) (2023)
- [An Unsupervised Approach for Periodic Source Detection in Time Series](https://mlsanity.com/paper/2406.00566/) (2024)

## Built on by (most cited first, 15 of 15)

- [egoPPG: Heart Rate Estimation from Eye-Tracking Cameras in Egocentric Systems to Benefit Downstream Vision Tasks](https://mlsanity.com/paper/2502.20879/) (2025)
- [MAESTRO : Adaptive Sparse Attention and Robust Learning for Multimodal Dynamic Time Series](https://mlsanity.com/paper/2509.25278/) (2025)
- [PulseLM: A Foundation Dataset and Benchmark for PPG-Text Learning](https://mlsanity.com/paper/2603.03331/) (2026)
- [A robust PPG foundation model using multimodal physiological supervision](https://mlsanity.com/paper/2606.07365/) (2026)
- [Hybrid Modeling of Photoplethysmography for Non-invasive Monitoring of Cardiovascular Parameters](https://mlsanity.com/paper/2511.14452/) (2025)
- [PENGUIN: General Vital Sign Reconstruction from PPG with Flow Matching State Space Model](https://mlsanity.com/paper/2602.03858/) (2026)
- [Self-Supervised and Topological Signal-Quality Assessment for Any PPG Device](https://mlsanity.com/paper/2509.12510/) (2025)
- [Temporal Cardiovascular Dynamics for Improved PPG-Based Heart Rate Estimation](https://mlsanity.com/paper/2510.27297/) (2025)
- [Towards Characterizing Knowledge Distillation of PPG Heart Rate Estimation Models](https://mlsanity.com/paper/2511.18829/) (2025)
- [Multi-site PPG: An In-the-Wild Physiological Dataset from Emerging Multi-site Wearables](https://mlsanity.com/paper/2605.17859/) (2026)
- [A Comprehensive Inference-Time Augmentation Framework in Physiological Signals: Application to PPG-Based AF Detection](https://mlsanity.com/paper/2606.10410/) (2026)
- [EgoHRV: Continuous Heart Rate Variability Estimation from Egocentric Systems for Autonomic Response and Skill Assessment](https://mlsanity.com/paper/2608.18711/) (2026)
- [Learning Where to Look: A Shared Relative-Alignment Module for Time-Series Forecasting and PPG-to-Vital-Sign Reconstruction](https://mlsanity.com/paper/2609.27473/) (2026)
- [Pulseflow: PPG Counterfactual Generation Via Latent Transport](https://mlsanity.com/paper/2609.33501/) (2026)
- [PPG-Bench: A Unified Benchmark for PPG Foundation Models](https://mlsanity.com/paper/2610.13435/) (2026)

## Abstract (arXiv)

Reflective photoplethysmography (PPG) has become the default sensing technique in wearable devices to monitor cardiac activity via a person's heart rate (HR). However, PPG-based HR estimates can be substantially impacted by factors such as the wearer's activities, sensor placement and resulting motion artifacts, as well as environmental characteristics such as temperature and ambient light. These and other factors can significantly impact and decrease HR prediction reliability. In this paper, we show that state-of-the-art HR estimation methods struggle when processing representative data from everyday activities in outdoor environments, likely because they rely on existing datasets that captured controlled conditions. We introduce a novel multimodal dataset and benchmark results for continuous PPG recordings during outdoor activities from 16 participants over 13.5 hours, captured from four wearable sensors, each worn at a different location on the body, totaling 216,hours. Our recordings include accelerometer, temperature, and altitude data, as well as a synchronized Lead I-based electrocardiogram for ground-truth HR references. Participants completed a round trip from Zurich to Jungfraujoch, a tall mountain in Switzerland over the course of one day. The trip included outdoor and indoor activities such as walking, hiking, stair climbing, eating, drinking, and resting at various temperatures and altitudes (up to 3,571,m above sea level) as well as using cars, trains, cable cars, and lifts for transport -- all of which impacted participants' physiological dynamics. We also present a novel method that estimates HR values more robustly in such real-world scenarios than existing baselines.