# Camera-Based HRV Prediction for Remote Learning Environments

arXiv: https://arxiv.org/abs/2305.04161 · HTML: https://mlsanity.com/paper/2305.04161/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 43 references that are in the map.
- First posted on arXiv 7 May 2023.

## Builds on (most cited first, 17 of 17)

- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [DeepPhys: Video-Based Physiological Measurement Using Convolutional Attention Networks](https://mlsanity.com/paper/1805.07888/) (2018)
- [Multi-Task Temporal Shift Attention Networks for On-Device Contactless Vitals Measurement](https://mlsanity.com/paper/2006.03790/) (2020)
- [Remote Photoplethysmograph Signal Measurement from Facial Videos Using Spatio-Temporal Networks](https://mlsanity.com/paper/1905.02419/) (2019)
- [PhysFormer: Facial Video-based Physiological Measurement with Temporal Difference Transformer](https://mlsanity.com/paper/2111.12082/) (2021)
- [RhythmNet: End-to-end Heart Rate Estimation from Face via Spatial-temporal Representation](https://mlsanity.com/paper/1910.11515/) (2019)
- [Remote Heart Rate Measurement from Highly Compressed Facial Videos: an End-to-end Deep Learning Solution with Video Enhancement](https://mlsanity.com/paper/1907.11921/) (2019)
- [Video-based Remote Physiological Measurement via Cross-verified Feature Disentangling](https://mlsanity.com/paper/2007.08213/) (2020)
- [EfficientPhys: Enabling Simple, Fast and Accurate Camera-Based Vitals Measurement](https://mlsanity.com/paper/2110.04447/) (2021)
- [VIPL-HR: A Multi-modal Database for Pulse Estimation from Less-constrained Face Video](https://mlsanity.com/paper/1810.04927/) (2018)
- [MMPD: Multi-Domain Mobile Video Physiology Dataset](https://mlsanity.com/paper/2302.03840/) (2023)
- [SCAMPS: Synthetics for Camera Measurement of Physiological Signals](https://mlsanity.com/paper/2206.04197/) (2022)
- [Efficient Remote Photoplethysmography with Temporal Derivative Modules and Time-Shift Invariant Loss](https://mlsanity.com/paper/2203.10882/) (2022)
- [Real-time Webcam Heart-Rate and Variability Estimation with Clean Ground Truth for Evaluation](https://mlsanity.com/paper/2012.15846/) (2020)
- [FFC-SE: Fast Fourier Convolution for Speech Enhancement](https://mlsanity.com/paper/2204.03042/) (2022)
- [MobilePhys: Personalized Mobile Camera-Based Contactless Physiological Sensing](https://mlsanity.com/paper/2201.04039/) (2022)
- [BYHE: A Simple Framework for Boosting End-to-end Video-based Heart Rate Measurement Network](https://mlsanity.com/paper/2207.01697/) (2022)

## Built on by (most cited first, 9 of 9)

- [PhysDrive: A Multimodal Remote Physiological Measurement Dataset for In-vehicle Driver Monitoring](https://mlsanity.com/paper/2507.19172/) (2025)
- [Camera-Based Remote Physiology Sensing for Hundreds of Subjects Across Skin Tones](https://mlsanity.com/paper/2404.05003/) (2024)
- [Non-Contact Health Monitoring During Daily Personal Care Routines](https://mlsanity.com/paper/2506.09718/) (2025)
- [Passive Heart Rate Monitoring During Smartphone Use in Everyday Life](https://mlsanity.com/paper/2503.03783/) (2025)
- [Memory-efficient Low-latency Remote Photoplethysmography through Temporal-Spatial State Space Duality](https://mlsanity.com/paper/2504.01774/) (2025)
- [Remote Bio-Sensing: Open Source Benchmark Framework for Fair Evaluation of rPPG](https://mlsanity.com/paper/2307.12644/) (2023)
- [M3PD Dataset: Dual-view Photoplethysmography (PPG) Using Front-and-rear Cameras of Smartphones in Lab and Clinical Settings](https://mlsanity.com/paper/2511.02349/) (2025)
- [Exploring Remote Physiological Signal Measurement under Dynamic Lighting Conditions at Night: Dataset, Experiment, and Analysis](https://mlsanity.com/paper/2507.04306/) (2025)
- [FacePhys: State of the Heart Learning](https://mlsanity.com/paper/2512.06275/) (2025)

## Abstract (arXiv)

In recent years, due to the widespread use of internet videos, remote photoplethysmography (rPPG) has gained more and more attention in the fields of affective computing. Restoring blood volume pulse (BVP) signals from facial videos is a challenging task that involves a series of preprocessing, image algorithms, and postprocessing to restore waveforms. Not only is the heart rate metric utilized for affective computing, but the heart rate variability (HRV) metric is even more significant. The challenge in obtaining HRV indices through rPPG lies in the necessity for algorithms to precisely predict the BVP peak positions. In this paper, we collected the Remote Learning Affect and Physiology (RLAP) dataset, which includes over 32 hours of highly synchronized video and labels from 58 subjects. This is a public dataset whose BVP labels have been meticulously designed to better suit the training of HRV models. Using the RLAP dataset, we trained a new model called Seq-rPPG, it is a model based on one-dimensional convolution, and experimental results reveal that this structure is more suitable for handling HRV tasks, which outperformed all other baselines in HRV performance and also demonstrated significant advantages in computational efficiency.