Cite this dataset
MS-Drive [Data set]. (n.d.). https://doi.org/10.1109/ICRA57385.2026.11695785
@misc{msdrivemsdrive,
title = {{MS-Drive}},
doi = {10.1109/ICRA57385.2026.11695785}
} Dataset
MS-Drive
synchronized RGB + 850 nm NIR in-vehicle driver face video with 3-lead ECG (Jeonbuk National University, ICRA 2026)
MS-Drive is a real-world driving rPPG dataset from Jeonbuk National University with 50 participants (28 male, 22 female, aged 20–34, Fitzpatrick types 2–6) who drove a research vehicle while an Intel RealSense D435i recorded synchronized RGB video (1920×1080, 30 fps) and NIR video (848×480, 30 fps, external 850 nm illuminator). Ground truth is 3-lead ECG at 128 Hz (Shimmer3) plus ECG-derived heart rate. It was introduced with the MS-rPPG paper (ICRA 2026, arXiv:2606.21115).
- License
MS-Drive academic-only licence (non-commercial; no modification or redistribution without written permission)
At a glance
- Type
- Dataset
- Host
- Not reported
- Licence
- MS-Drive academic-only licence (non-commercial; no modification or redistribution without written permission)
- Access
- Gated / on request
- Subjects
- 50
- PPG sites
- Not reported
- Setting
- Not reported
- Wrist
- Not reported
- Raw waveforms
- Not reported
- Tasks
- Not reported
- Models
- Updated
- Oct 29, 2026
Original description
Remote photoplethysmography (rPPG) is a camera-based technique for measuring physiological signals, particularly cardiac activity. From the remotely measured signals, heart rate can be estimated, which is crucial for health monitoring. In this study, we investigate a driver health monitoring system based on remote heart rate estimation. However, driving environments represent uncontrolled settings where videos are subject to varying illumination conditions and frequent head movements. We introduce MS-rPPG, a multi-spectral framework that combines RGB with near-infrared (NIR) face video to alleviate rPPG estimation under challenging driving conditions. To combine the complementary features from two spectral videos, we propose a cross-spectral linear modulation (CSLM) strategy based on frequency-domain analysis. Moreover, we introduce MS-Mamba, a novel state space model designed to effectively model long-range temporal dependencies while jointly capturing cross-channel interactions between multi-spectral features. We collected a real-world dataset called MS-Drive, which was recorded from 50 participants while driving the vehicle. The proposed method was evaluated on the MR-NIRP Car dataset and MS-Drive datasets. The experimental results indicate that MS-rPPG shows better robustness and heart rate estimation accuracy than previous methods, highlighting its promise for driver health monitoring. The codes are available at github.com/ziiho08/MS-rPPG.
Verbatim fromSemantic Scholar· Abstract via Semantic Scholar; © the authors/publisher
Key numbers
50
Subjects
2
Reference labels
ECG, HR
How it works
Inputs
MS-Drive
MS-rPPG
Outputs / tasks
- Reference labels
- ecg, hr
Models using this dataset· 1
#78 of 117 most-used datasets by published papers and models · #39 of 54 ECG · #4 of 8 rPPG
Cite the ChatPPG record
Cite the dated snapshot 2026-10 for a fixed, reproducible version. Please also cite the original publications listed above.
APA
ChatPPG. (2026). MS-Drive: dataset record (Version 2026-10) [Data set]. ChatPPG Research Hub. https://chatppg.com/research/datasets/ms-drive
BibTeX
@misc{chatppg_dataset_ms_drive,
author = {{ChatPPG}},
title = {{MS-Drive: dataset record}},
year = {2026},
month = oct,
version = {2026-10},
howpublished = {\url{https://chatppg.com/research/datasets/ms-drive}},
note = {ChatPPG Research Hub, Snapshot 2026-10, https://chatppg.com/research/snapshots/2026-10}
}CITATION.cff
cff-version: 1.2.0 message: "If you use this resource, please cite it as below." type: dataset title: "MS-Drive: dataset record" authors: - name: "ChatPPG" url: "https://chatppg.com/research/datasets/ms-drive" repository-artifact: "https://chatppg.com/research/snapshots/2026-10" version: "2026-10" date-released: "2026-10-29"
History
Record updated