# Multi-site PPG: An In-the-Wild Physiological Dataset from Emerging Multi-site Wearables

arXiv: https://arxiv.org/abs/2605.17859 · HTML: https://mlsanity.com/paper/2605.17859/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 49 references that are in the map.
- First posted on arXiv 18 May 2026.

## Builds on (most cited first, 14 of 14)

- [Time Series Classification from Scratch with Deep Neural Networks: A Strong Baseline](https://mlsanity.com/paper/1611.06455/) (2016)
- [Scaling Wearable Foundation Models](https://mlsanity.com/paper/2410.13638/) (2024)
- [TROIKA: A General Framework for Heart Rate Monitoring Using Wrist-Type Photoplethysmographic Signals During Intensive Physical Exercise](https://mlsanity.com/paper/1409.5181/) (2014)
- [Pulse-PPG: An Open-Source Field-Trained PPG Foundation Model for Wearable Applications Across Lab and Field Settings](https://mlsanity.com/paper/2502.01108/) (2025)
- [SCAMPS: Synthetics for Camera Measurement of Physiological Signals](https://mlsanity.com/paper/2206.04197/) (2022)
- [Photoplethysmography-Based Heart Rate Monitoring in Physical Activities via Joint Sparse Spectrum Reconstruction](https://mlsanity.com/paper/1503.00688/) (2015)
- [WildPPG: A Real-World PPG Dataset of Long Continuous Recordings](https://mlsanity.com/paper/2412.17540/) (2024)
- [BeliefPPG: Uncertainty-aware Heart Rate Estimation from PPG signals via Belief Propagation](https://mlsanity.com/paper/2306.07730/) (2023)
- [PPG-Distill: Efficient Photoplethysmography Signals Analysis via Foundation Model Distillation](https://mlsanity.com/paper/2509.19215/) (2025)
- [EnhancePPG: Improving PPG-based Heart Rate Estimation with Self-Supervision and Augmentation](https://mlsanity.com/paper/2412.17860/) (2024)
- [A Dataset and Toolkit for Multiparameter Cardiovascular Physiology Sensing on Rings](https://mlsanity.com/paper/2505.04172/) (2025)
- [Multi-Head Cross-Attentional PPG and Motion Signal Fusion for Heart Rate Estimation](https://mlsanity.com/paper/2210.11415/) (2022)
- [A General Framework for Generative Self-supervised Learning in Non-invasive Estimation of Physiological Parameters Using Photoplethysmography](https://mlsanity.com/paper/2604.22780/) (2026)
- [Robust Heart Rate Detection via Multi-Site Photoplethysmography](https://mlsanity.com/paper/2412.17538/) (2024)

## Abstract (arXiv)

Wearables are widely used for mobile health monitoring, and photoplethysmography (PPG) is a key sensing modality for heart rate and related physiological measurements. However, public in-the-wild PPG datasets remain largely wrist-centric or limited to short, controlled studies, constraining research on emerging wearable form factors. We present Multi-site PPG, an in-the-wild physiological dataset collected from four custom-developed unobtrusive wearables: a smart earring, ring, watch, and necklace. Each device records green and infrared reflective PPG, 3-axis acceleration, and temperature with timestamps for cross-device alignment, while a Polar H10 chest strap provides reference electrocardiogram (ECG). Participants wore the devices for multiple days during daytime activities while continuing their normal routines. The dataset contains over 350 hours of raw data and 230-290 hours of modeling-ready 8-second windows per wearable. We benchmark heuristic, supervised, and self-supervised heart-rate estimation methods, showing substantial body-site differences: the best methods achieve mean absolute errors (MAEs) of 2.30 bpm on the earring, 5.13 bpm on the ring, 8.37 bpm on the watch, and 8.68 bpm on the necklace. We further analyze motion effects and evaluate multi-site and PPG-accelerometer fusion, demonstrating the dataset's value for robust physiological sensing across emerging wearable form factors.