HCLGMay 18

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

arXiv:2605.1785958.7
Predicted impact top 22% in HC · last 90 daysOriginality Incremental advance
AI Analysis

This dataset enables research on physiological sensing from emerging wearable form factors beyond the wrist, addressing a gap in public in-the-wild PPG data.

The authors introduce a multi-site PPG dataset from earring, ring, watch, and necklace wearables collected in naturalistic settings, totaling over 350 hours. Benchmarking shows best MAEs of 2.30 bpm on earring, 5.13 bpm on ring, 8.37 bpm on watch, and 8.68 bpm on necklace, highlighting body-site differences.

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.

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