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PPGI-Toolbox

version 0.0.1.1 (639 KB) by Borze
A MATLAB toolbox for Photoplethysmography Imaging

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Updated 17 Jul 2019

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A MATLAB Toolbox for Photoplethysmography Imaging

by Christian S. Pilz,
Aachen, 2019

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.

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]
- LGI Multi Session [1,2,3]

References:

1. Christian S. Pilz, Vladimir Blazek, Steffen Leonhardt, On the Vector Space in Photoplethysmography Imaging, Preprint: arXiv:1903.03316 [cs.CV], 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 technique for opto-electronic assessment of skin perfusion. PhD thesis, RWTH Aachen University, 2008.

10. S. Bobbia, R. Macwan, Y. Benezeth, A. Mansouri, J. Dubois, "Unsupervised skin tissue segmentation for remote photoplethysmography", Pattern Recognition Letters, 2017.

Cite As

Christian S. Pilz, Vladimir Blazek, Steffen Leonhardt, On the Vector Space in Photoplethysmography Imaging, arXiv:1903.03316 [cs.CV], 2019

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Updates

0.0.1.1

correction of references in the description

MATLAB Release Compatibility
Created with R2016b
Compatible with any release
Platform Compatibility
Windows macOS Linux