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

TL;DR

This paper introduces Multi-site PPG, a comprehensive in-the-wild dataset from four novel wearable devices, enabling advanced research in physiological sensing and heart rate estimation across diverse body sites.

Contribution

It provides a large, multi-device dataset with synchronized PPG, accelerometer, and ECG data from emerging wearables, and benchmarks various heart-rate estimation methods.

Findings

Best heart-rate estimation MAE: 2.30 bpm on earring

Significant body-site differences in estimation accuracy

Motion effects and sensor fusion improve robustness

Abstract

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.