EMPD: An Event-based Multimodal Physiological Dataset for Remote Pulse Wave Detection
This provides a crucial resource for developing robust algorithms in neuromorphic physiological monitoring, addressing motion artifacts and limited temporal resolution in remote photoplethysmography.
The authors tackled the problem of remote pulse wave detection by introducing EMPD, the first event-based multimodal physiological dataset, which includes 193 records from 83 subjects covering heart rates from 40-110 BPM under resting and post-exercise conditions.
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