Математика и математическое моделирование. 2015; : 66-80
Метод бесконтактной оценки паттерна дыхания человека при помощи стереопары
Аннотация
Список литературы
1. Алёхин М.Д., Анищенко Л.Н., Корчагина Д.А. Метод биорадиолокации в анализе перемещений грудной клетки при спокойном дыхании. Биомедицинская радиоэлектроника. 2009. № 10. C. 56-61. Режим доступа: http://www.radiotec.ru/catalog.php?cat=jr6&art=6827 (дата обращения: 15.06.2015).
2. Heinrich A., Heesch F. van. Video based actigraphy and breathing monitoring from the bedside table of shared beds// Journal of Ambient Intelligence and Humanized Computing. 2014. № 6. Pp. 107-120. DOI: 10.1007/s12652-014-0247-6. Режим доступа: http://link.springer.com/article/10.1007%2Fs12652-014-0247-6#page-2 (дата обращения 15.06.2015).
3. Wang Ch. W., Hunter A. Unconstrained Video Monitoring of Breathing Behavior and Application to Diagnosis of Sleep Apnea// IEEE Transactions on Biomedical Engineering. 2014. Vol. 61. No 2. Pp. 396 - 404. DOI: 10.1109/TBME.2013.2280132. Режим доступа: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6587794 (дата обращения: 25.06.2015).
4. Middlebury Stereo Datasets. http://vision.middlebury.edu/stereo/data/ (дата обращения: 15.06.2015)
5. Mei X., Sun X.; Zhou M.; Jiao S.; Wang H.; Zhang X. On Building an Accurate Stereo Matching System on Graphics Hardware // 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops). Barcelona. 2011. Рр. 467 - 474. DOI: 10.1109/ICCVW.2011.6130280. (http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6130280).
6. Тan P., Monasse P. Stereo Disparity through Cost Aggregation with Guided Filter// Image Processing On Line. 2014, N. 4. Pp. 252-275. http://dx.doi.org/10.5201/ipol.2014.78
7. Zhang K., Lu J. Cross-Based Local Stereo Matching Using Orthogonal Integral Images// IEEE Transactions on Circuits and Systems for Video Technology. 2009. Vol. 19, Issue 7. Pp. 1073 - 1079. DOI: 10.1109/TCSVT.2009.2020478. Режим доступа: http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=4811952&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D4811952 (дата обращения 15.06.2015).
8. Fang J., Varbanescu A. L. Accelerating Cost Aggregation for Real-Time Stereo Matching// IEEE 18th International Conference on Parallel and Distributed Systems (ICPADS). Singapore, 2012. Pp. 472 - 481. DOI: 10.1109/ICPADS.2012.71. Режим доступа: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6413661 (дата обращения: 15.06.2015).
Mathematics and Mathematical Modeling. 2015; : 66-80
A Stereo Pair Based Method for Contactless Evaluation of the Human Breathing Pattern
Abstract
The development of contactless monitoring methods of human vital signs is an important goal for modern medicine. The particular relevance of this issue appears with the control of the patient at home on their own, for example, to estimate the parameters of breathing during sleep, quality assessment and identification of various kinds of sleep disorders, such as, for example, sleep apnea disorder (a condition, which is characterized by the cessation of pulmonary ventilation more than for 10 seconds and fall of blood oxygen saturation).
In this article we have implemented and tested an algorithm for non-contact monitoring of breathing pattern by two entrenched webcams aimed at the person. The algorithm is based on using the methods of computer vision and processing of video sequences.
Authors pay particular attention to disparity map construction approaches and improving the signal / noise ratio by a combination of known functions comparing the intensity of pixels: AD - a function of absolute differences, and Census function, comparing bit strings of investigated image regions.
An important role in the noise minimization plays a simple, but effective assumption for aggregation, the gist of which is that pixels having similar intensity belong to the same structures in the image, and hence have a similar disparity. The variability of input parameters of the method and the ability to adjust the number of iterations provide accurate disparity maps for the input image of almost any quality (testing was conducted for webcams CBR CW 833M).
The main result of this approach is the breathing profile based on the reconstructed depth maps, reflecting the respiration rate of the person under examination and presenting data on the amplitude variations of his chest.
The main difference of the proposed method from other publications is a high accuracy and the breath profile calculation in real-time. It was achieved through OpenCL technology and parallel computations using the graphics card.
The algorithm was tested on a variety of subjects with anthropomorphic characteristics and types of breathing to investigate the limited application of the proposed method in practice.
References
1. Alekhin M.D., Anishchenko L.N., Korchagina D.A. Metod bioradiolokatsii v analize peremeshchenii grudnoi kletki pri spokoinom dykhanii. Biomeditsinskaya radioelektronika. 2009. № 10. C. 56-61. Rezhim dostupa: http://www.radiotec.ru/catalog.php?cat=jr6&art=6827 (data obrashcheniya: 15.06.2015).
2. Heinrich A., Heesch F. van. Video based actigraphy and breathing monitoring from the bedside table of shared beds// Journal of Ambient Intelligence and Humanized Computing. 2014. № 6. Pp. 107-120. DOI: 10.1007/s12652-014-0247-6. Rezhim dostupa: http://link.springer.com/article/10.1007%2Fs12652-014-0247-6#page-2 (data obrashcheniya 15.06.2015).
3. Wang Ch. W., Hunter A. Unconstrained Video Monitoring of Breathing Behavior and Application to Diagnosis of Sleep Apnea// IEEE Transactions on Biomedical Engineering. 2014. Vol. 61. No 2. Pp. 396 - 404. DOI: 10.1109/TBME.2013.2280132. Rezhim dostupa: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6587794 (data obrashcheniya: 25.06.2015).
4. Middlebury Stereo Datasets. http://vision.middlebury.edu/stereo/data/ (data obrashcheniya: 15.06.2015)
5. Mei X., Sun X.; Zhou M.; Jiao S.; Wang H.; Zhang X. On Building an Accurate Stereo Matching System on Graphics Hardware // 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops). Barcelona. 2011. Rr. 467 - 474. DOI: 10.1109/ICCVW.2011.6130280. (http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6130280).
6. Tan P., Monasse P. Stereo Disparity through Cost Aggregation with Guided Filter// Image Processing On Line. 2014, N. 4. Pp. 252-275. http://dx.doi.org/10.5201/ipol.2014.78
7. Zhang K., Lu J. Cross-Based Local Stereo Matching Using Orthogonal Integral Images// IEEE Transactions on Circuits and Systems for Video Technology. 2009. Vol. 19, Issue 7. Pp. 1073 - 1079. DOI: 10.1109/TCSVT.2009.2020478. Rezhim dostupa: http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=4811952&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D4811952 (data obrashcheniya 15.06.2015).
8. Fang J., Varbanescu A. L. Accelerating Cost Aggregation for Real-Time Stereo Matching// IEEE 18th International Conference on Parallel and Distributed Systems (ICPADS). Singapore, 2012. Pp. 472 - 481. DOI: 10.1109/ICPADS.2012.71. Rezhim dostupa: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6413661 (data obrashcheniya: 15.06.2015).
