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Paper
G. Boccignone, D. Conte, V. Cuculo, A. D’Amelio, G. Grossi and R. Lanzarotti, "An Open Framework for Remote-PPG Methods and their Assessment," in IEEE Access, doi: 10.1109/ACCESS.2020.3040936.

https://ieeexplore.ieee.org/document/9272290

Package pyVHR (short for Python framework for Virtual Heart Rate) is a comprehensive framework for studying methods of pulse rate estimation relying on remote photoplethysmography (rPPG). The methodological rationale behind the framework is that in order to study, develop and compare new rPPG methods in a principled and reproducible way, the following conditions should be met: i) a structured pipeline to monitor rPPG algorithms' input, output, and main control parameters; ii) the availability and the use of multiple datasets; iii) a sound statistical assessment of methods' performance. pyVHR allows to easily handle rPPGmethods and data, while simplifying the statistical assessment. Its main features lie in the following.

• Analysis-oriented. It constitutes a platform for experiment design, involving an arbitrary number of methods applied to multiple video datasets. It provides a systemic end-to-end pipeline, allowing to assess different rPPG algorithms, by easily setting parameters and meta-parameters.
• Openness. It comprises both method and dataset factory, so to easily extend the pool of elements to be evaluatedwith newly developed rPPG methods and any kind of videodatasets.
• Robust assessment. The outcomes are arranged intostructured data ready for in-depth analyses. Performance comparison is carried out based on robust nonparametric statistical tests.

Eight well-known rPPG methods, namely ICA, PCA, GREEN,CHROM, POS, SSR, LGI, PBV, are evaluated through extensive experiments across five public video datasets, i.e. PURE, LGI, USBC, MAHNOB and COHFACE, and subsequent nonparametric statistical analysis.

Install the dependency first:

$ pip install numpy

then, install the library directly into an activated virtual environment:

$ pip install pyvhr

or download from source and install via:

$ python setup.py install

The notebooks folder contains three different Jupyter notebooks:

Basic usage
Shows the basic steps to process video for heart rate estimation trough remote PPG methods.
[Source] [Demo]

Extend the framework
This notebook shows how to extend the framework with additional datasets and methods.
[Source] [Demo]

Statistical analysis
Includes statistical analysis and the results presented in the paper (see Reference) applying all the available methods on six different datasets.
[Source] [Demo]

The framework contains the implementation of the most common methods for remote-PPG measurement, and are located in the methods folder.
Currently implemented methods with reference publications are:

Green

Verkruysse, W., Svaasand, L. O., & Nelson, J. S. (2008). Remote plethysmographic imaging using ambient light. Optics express, 16(26), 21434-21445.

CHROM

Benezeth, Y., Li, P., Macwan, R., Nakamura, K., Gomez, R., & Yang, F. (2018, March). Remote heart rate variability for emotional state monitoring. In 2018 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI) (pp. 153-156). IEEE.

ICA

Poh, M. Z., McDuff, D. J., & Picard, R. W. (2010). Non-contact, automated cardiac pulse measurements using video imaging and blind source separation. Optics express, 18(10), 10762-10774.

LGI

Pilz, C. S., Zaunseder, S., Krajewski, J., & Blazek, V. (2018). Local group invariance for heart rate estimation from face videos in the wild. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (pp. 1254-1262).

PBV

De Haan, G., & Van Leest, A. (2014). Improved motion robustness of remote-PPG by using the blood volume pulse signature. Physiological measurement, 35(9), 1913.

PCA

Lewandowska, M., Rumiński, J., Kocejko, T., & Nowak, J. (2011, September). Measuring pulse rate with a webcam—a non-contact method for evaluating cardiac activity. In 2011 federated conference on computer science and information systems (FedCSIS) (pp. 405-410). IEEE.

POS

Wang, W., den Brinker, A. C., Stuijk, S., & de Haan, G. (2016). Algorithmic principles of remote PPG. IEEE Transactions on Biomedical Engineering, 64(7), 1479-1491.

SSR

Wang, W., Stuijk, S., & De Haan, G. (2015). A novel algorithm for remote photoplethysmography: Spatial subspace rotation. IEEE transactions on biomedical engineering, 63(9), 1974-1984.

Interfaces for six different datasets are provided in the datasets folder. Once the datasets are obtained, the respective files must be edited to match the correct path.
Currently supported datasets are:

COHFACE

https://www.idiap.ch/dataset/cohface

LGI-PPGI

https://github.com/partofthestars/LGI-PPGI-DB

MAHNOB-HCI

https://mahnob-db.eu/hci-tagging/

PURE

https://www.tu-ilmenau.de/en/neurob/data-sets-code/pulse/

UBFC1

https://sites.google.com/view/ybenezeth/ubfcrppg

UBFC2

https://sites.google.com/view/ybenezeth/ubfcrppg

If you use this code, please cite the paper:

@article{Boccignone2020,
  doi = {10.1109/access.2020.3040936},
  url = {https://doi.org/10.1109/access.2020.3040936},
  year = {2020},
  publisher = {Institute of Electrical and Electronics Engineers ({IEEE})},
  pages = {1--1},
  author = {Giuseppe Boccignone and Donatello Conte and Vittorio Cuculo and Alessandro D’Amelio and Giuliano Grossi and Raffaella Lanzarotti},
  title = {An Open Framework for Remote-{PPG} Methods and their Assessment},
  journal = {{IEEE} Access}
}

This project is licensed under the GPL-3.0 License - see the LICENSE file for details