# PPGI-Toolbox
<p align="center"><img width=60% src="https://github.com/partofthestars/PPGI-Toolbox/blob/master/media/ico/Logo.jpg"></p>
<p align="center"><b>A MATLAB Toolbox for Photoplethysmography Imaging</b></p>
<p align="center">by Christian S. Pilz,<br>
Aachen, 2019</p>
<p align="center">Version beta0.1</p>
<p align="center"><img width=10% src="https://github.com/partofthestars/PPGI-Toolbox/blob/master/media/ico/beta.jpg"></p>
## News
**04.04.2022**<br>
I uploaded the full implementation of the diffusion process model [3,4].<br>
An example implementation of a stochastic oscillator is added as well:
<p align="center"><img width=60% src="https://github.com/partofthestars/PPGI-Toolbox/blob/master/media/results/oscillator.png"></p>
**29.11.2021**<br>
Our work was recently featured in the following Nature article about digital medicine (npj Digit. Med.):<br>
*Dasari, A., Prakash, S.K.A., Jeni, L.A. et al.. Evaluation of biases in remote photoplethysmography methods. npj Digit. Med. 4, 91 (2021).*
[https://doi.org/10.1038/s41746-021-00462-z](https://doi.org/10.1038/s41746-021-00462-z)
## Supported by
| [![VideoBlocks](https://github.com/partofthestars/PPGI-Toolbox/blob/master/media/ico/cancontrols.png)](http://cancontrols.com) | [![GraphicStock](https://github.com/partofthestars/PPGI-Toolbox/blob/master/media/ico/IXP.png)](http://ixp-duesseldorf.de) | [![AudioBlocks](https://github.com/partofthestars/PPGI-Toolbox/blob/master/media/ico/UKAachen.png)](https://www.ukaachen.de/kliniken-institute/klinik-fuer-psychiatrie-psychotherapie-und-psychosomatik/forschung/neuropsychologie.html) | [![AudioBlockss](https://github.com/partofthestars/PPGI-Toolbox/blob/master/media/ico/MedIT_RWTH.png)](https://www.medit.hia.rwth-aachen.de) |
|:---:|:---:|:---:| :---:|
## Funded by
| [![BMBF](https://github.com/partofthestars/PPGI-Toolbox/blob/master/media/ico/efre_eu_logo.png)](https://ec.europa.eu/regional_policy/en/funding/erdf/) | [![EFRE](https://github.com/partofthestars/PPGI-Toolbox/blob/master/media/ico/BMBF.png)](http://bmbf.de) | [![EFRE.NRW](https://github.com/partofthestars/PPGI-Toolbox/blob/master/media/ico/efre_nrw_logo.jpg)](http://efre.nrw.de) |
|:---:|:---:|:---:|
## Introduction
During the last years measuring blood volume changes and heart rate measurements from facial images
gained attention at top computer vision conferences frequently. Most of these contributions focus
on how to cope with motion like head pose variations and facial expressions since any kind of motion on a specific
skin region of interest will destroy the raw signal in a way that no reliable information can be extracted anymore.
Beside from being able to estimate vitality parameters like heart rate and respiration, the functional survey of wounds
as well as quantification of allergic skin reaction are further topics of discovered employments of skin blood
perfusion analysis. Recently, prediction of emotional states, stress, fatigue and sickness became interesting new achievements in this area, pushing the focus of this technology further towards human-machine interaction.
In contrast to the genuine medical use-case of the technology, in computer vision and human-machine interaction we can’t expect any cooperative behavior of the user without introducing lack of convenience and a reduction of
the general user acceptance. Further, beyond any well tempered clinical and laboratory like scenarios, the majority
application will face strong challenging environmental changes and differences much more quite common. Thus,
there’s an emerging demand to produce better features and models significant more robust to nuisance factors, still
preserving the desired target information. To reach such a formulation a fundamental profound understanding of the
underlying optical and mathematical properties is one of the current foci of this research discipline.
<br><br>
<p align="center"><img width=75% src="https://github.com/partofthestars/PPGI-Toolbox/blob/master/media/ico/cvpr2018.jpg"></p>
<i><b>Figure 1.</b> Rigid and non-rigid facial motions act as nuisance factors
on the tiny blood volume changes inherently destroying the target
information of heart rate under the conventional formulation of the
problem. Utilizing features invariant with respect to the action of the group of nuisance
transformations making it possible to estimate heart rate information under everyday facial motions. As illustrated for the above head motions, the green channel information doesn’t yield to reasonable heart rate information in the frequency domain. However, in case we're able to extract invariant features we obtain a clear signal in both time
and frequency domain.</i>
## Example Data
The example data can be download by the following link:<br>
https://www.dropbox.com/s/vv6ethy5az16wt4/example_data.mat?dl=0 <br>
(NOTE: Leave me a message in case the file isn't available anymore via dropbox!)<br>
Alternative download location: https://gw.cancontrols.com/LGI_DATABASE/toolbox/example_data.mat
Place the example_data.mat file into ./media/data/ folder.<br>
The example_data.mat contains the reference finger pulse oximeter waveform (PPG)
and the color image data of a face finder detection result in the rgb cell.
## Tests
In the tests directory there're separate example Matlab scripts. One for each algorithm respectively.
Initially the startup.m script must be executed to add all needed directories.
As second step executing test_skin.m will perform skin segmentation on the example_data.mat in case it was
downloaded and placed into the ./media/data/ folder. The obtained skin pixels will be stored into the example_data.mat file
and can be reused to test all other algorithm scripts.
## Algorithms
- Channel Mean (G) [8,9]
- Spatial Subspace Rotation (SSR) [6]
- Algorithmic Principles of Remote PPG (POS)[5]
- Local Group Invariance (LGI) [1,2]
- Diffusion Process (DP) [3,4]
- Riemannian-PPGI (SPH) [1]
## Evaluation
- Correlation
- Bland-Altman
- RMSE/ MSE
- SNR [7]
## Databases
- UBFC-RPPG [10]: [Download link](https://sites.google.com/view/ybenezeth/ubfcrppg)
- LGI Multi Session [1,2,3]: [Download link](https://github.com/partofthestars/LGI-PPGI-DB)
## References
1. Christian S. Pilz, Ibtissem Ben Makhlouf, Vladimir Blazek, Steffen Leonhardt,
On the Vector Space in Photoplethysmography Imaging,
The IEEE International Conference on Computer Vision (ICCV) Workshops,
Seoul/ South Korea, 2019
2. Christian S. Pilz, S. Zaunseder, J. Krajewski, V. Blazek,
Local Group Invariance for Heart Rate Estimation from Face Videos in the Wild,
The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, pp.1254-1262, Salt Lake City, 2018
3. Christian S. Pilz, Jarek Krajewski, Vladimir Blazek.
On the Diffusion Process for Heart Rate Estimation from Face Videos under Realistic Conditions.
Pattern Recognition: 39th German Conference, GCPR 2017, Basel, Switzerland.
Proceedings (Lecture Notes in Computer Science), pp. 361-373, Springer, 2017
4. Christian S. Pilz, Sebastian Zaunseder, Ulrich Canzler, Jarek Krajewski.
Heart rate from face videos under realistic conditions for advanced driver monitoring.
Current Directions in Biomedical Engineering, De Gruyter, Berlin, pp. 483–487, 2017.
5. Wang, W., den Brinker, A. C., Stuijk, S., & de Haan, G. (2017). Algorithmic principles of remote PPG. IEEE Transactions on Biomedical Engineering, 64(7), 1479-1491
6. W. Wang, S. Stuijk and G. de Haan, "A Novel Algorithm for Remote Photoplethysmography: Spatial Subspace Rotation," in IEEE Transactions on Biomedical Engineering, vol. 63, no. 9, pp. 1974-1984, Sept. 2016.
7. De Haan, G., & Jeanne, V. (2013). Robust pulse rate from chrominance-based rPPG. IEEE Transactions on Biomedical Engineering, 60(10), 2878-2886
8. Verkruysse, W., Svaasand, L. O., & Nelson, J. S. (2008). Remote plethysmographic imaging using ambient light. Optics express, 16(26), 21434-21445
9. M. Hülsbusch. A functional imaging tech
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用于光体积描记成像 的 MATLAB 工具箱_MATLAB_代码_下载
共64个文件
m:26个
png:17个
md:15个
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算法 通道平均值 (G) [8,9] 空间子空间旋转 (SSR) [6] 远程PPG(POS)算法原理[5] 局部组不变性 (LGI) [1,2] 扩散过程 (DP) [3,4] 黎曼-PPGI (SPH) [1] 评估 相关性 布兰德-奥特曼 RMSE/MSE 信噪比 [7] 数据库 UBFC-RPPG [10]: LGI 多会话 [1,2,3]:
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PPGI-Toolbox-master.zip (64个子文件)
PPGI-Toolbox-master
README.md 11KB
tests
README.md 2KB
test_pos.m 979B
test_ssr.m 1KB
test_skin.m 795B
test_lgi.m 1KB
test_bland_altman.m 2KB
test_spherical_mean.m 1KB
test_stochastic_resonator.m 2KB
test_diffusion_process.m 2KB
test_channel_mean.m 1KB
LICENSE 7KB
startup.m 1009B
media
ico
README.md 172B
efre_eu_logo.png 65KB
UKAachen.png 15KB
BMBF.png 6KB
IXP.png 18KB
cancontrols.png 78KB
Logo.jpg 87KB
cvpr2018.jpg 115KB
efre_nrw_logo.jpg 37KB
beta.jpg 52KB
MedIT_RWTH.png 65KB
README.md 172B
results
README.md 183B
LGI
LGI_Office_Head_Resting_rmse.png 13KB
README.md 215B
LGI_City_rmse.png 11KB
LGI_City_pearson.png 10KB
LGI_GYM_rmse.png 11KB
LGI_Head_Rotation_pearson.png 12KB
LGI_Office_Head_Resting_pearson.png 13KB
LGI_Head_Rotation_rmse.png 11KB
LGI_GYM_pearson.png 11KB
oscillator.png 11KB
UBFC
README.md 216B
ubfc_pearson.png 12KB
ubfc_rmse.png 13KB
data
README.md 457B
VERSION 91B
lib
evaluation
README.md 183B
ground_truth_stats.m 2KB
bland_altman.m 20KB
README.md 460B
utils
README.md 198B
circular_buffer.m 4KB
skin.m 1KB
bandpass_filter.m 2KB
algorithm
README.md 355B
models
README.md 308B
stochastic_oscillator
simulate_periodic_data.m 2KB
lti_disc.m 2KB
simulate_and_estimate.m 1KB
diffusion_process
README.md 172B
diffusion_process.m 3KB
separate.m 11KB
track.m 14KB
features
projection_orthogonal_to_skin.m 2KB
spatial_subspace_rotation.m 3KB
README.md 267B
channel_mean.m 1KB
spherical_mean.m 1KB
local_group_invariance.m 2KB
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