RLAP is a CCNU remote-learning affect and physiology corpus with 58 participants, multi-codec face video, and synchronized BVP. Distribution requires a Data Usage Agreement via the public GitHub repository.
Original description
The Remote Learning Affect and Physiologic (RLAP) dataset is a dataset applied to remote learning affect and engagement, which contains learners' blood volume pulse (BVP) signals that are highly synchronized. This dataset is suitable for training neural rPPG algorithms.
In recent years, remote physiological sensing has received increasing attention, especially the algorithms using neural networks. However, training a model for extracting physiological signals is not easy. The lack of high-quality datasets limits the development of advanced algorithms, and many models have to add some modules to fight against the "noise" in the dataset. The main noises are: offset caused by no strict synchronization between cameras and physiological signal sensors; data loss during video compression due to device's unsupported or incorrect settings. The RLAP dataset is collected using PhysRecorder to minimize any adverse factors for model training as much as possible. According to PhysBench's experimental results, this is known as the best training set so far.
Verbatim fromGitHub· Description from the repository README; licence: NOASSERTION
Key numbers
How it works
No model in the hub reports results on this dataset yet.
- Reference labels
- hr
Models trained or evaluated on it
FAQ
How many subjects are in RLAP?
RLAP has data from 58 subjects, as listed in its research record.
What signals does RLAP include?
RLAP includes video and PPG data.
How do I get RLAP, and what is its licence?
It is available from https://github.com/KegangWangCCNU/RLAP-dataset. Access is controlled by the host (for example registration, credentialing or a data use agreement), so check the source page for the steps. No licence is recorded for it here, so check the terms on the source page before you use it.
Cite
No citation metadata yet.
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). RLAP: dataset record (Version 2026-10) [Data set]. ChatPPG Research Hub. https://chatppg.com/research/datasets/rlap
BibTeX
@misc{chatppg_dataset_rlap,
author = {{ChatPPG}},
title = {{RLAP: dataset record}},
year = {2026},
month = oct,
version = {2026-10},
howpublished = {\url{https://chatppg.com/research/datasets/rlap}},
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: "RLAP: dataset record" authors: - name: "ChatPPG" url: "https://chatppg.com/research/datasets/rlap" repository-artifact: "https://chatppg.com/research/snapshots/2026-10" version: "2026-10" date-released: "2026-10-24"
History
Record updated
- BP4D-Dyadic / Inter-StanceSame signals: PPG, Videodyadic multimodal corpus with face video + finger PPG + continuous BP (Binghamton, FG 2026)90 subjects · PPG · EDA · BP
- COHFACESame signals: PPG, VideoIdiap rPPG face videos + BVP/respiration (40 subjects)40 subjects · PPG · Resp · Video
- EMPDSame signals: PPG, Videoevent-camera multimodal physiological dataset (wrist events + face RGB)83 subjects · PPG · Video · event
- iBVPSame signals: PPG, VideoRGB-Thermal rPPG with high-resolution signal-quality labels (UCL)PPG · Video · thermal