EMPD: An Event-based Multimodal Physiological Dataset for Remote Pulse Wave Detection

TL;DR

The paper introduces EMPD, a new multimodal event-based dataset for remote pulse wave detection, addressing motion artifacts and resolution issues in traditional methods.

Contribution

It provides the first benchmark dataset using event cameras for non-contact physiological sensing with synchronized multimodal data.

Findings

Contains 193 records from 83 subjects across various heart rates.

Includes synchronized data from event cameras, RGB cameras, and pulse oximeters.

Enables development of robust neuromorphic physiological monitoring algorithms.

Abstract

Remote photoplethysmography (rPPG) based on traditional frame-based cameras often struggles with motion artifacts and limited temporal resolution. To address these limitations, we introduce EMPD (Event-based Multimodal Physiological Dataset), the first benchmark dataset specifically designed for non-contact physiological sensing via event cameras. The dataset leverages a laser-assisted acquisition system where a high-coherence laser modulates subtle skin vibrations from the radial artery into significant signals detectable by a neuromorphic sensor. The hardware platform integrates a high-resolution event camera to capture micro-motions and intensity transients, an industrial RGB camera to provide traditional rPPG benchmarks, and a clinical-grade pulse oximeter to record ground truth PPG waveforms. EMPD contains 193 valid records collected from 83 subjects, covering a wide heart rate range (40-110 BPM) under both resting and post-exercise conditions. By providing precisely synchronized multimodal data with microsecond-level temporal precision, EMPD serves as a crucial resource for developing robust algorithms in the field of neuromorphic physiological monitoring. The dataset is publicly available at: https://doi.org/10.5281/zenodo.18765701