Non-contact respiration monitoring using optical sensors

Main Article Content

Олег Костянтинович Боділовський

Abstract

The main goal of this paper is to develop classification of non-contact respiration monitoring approaches and proposal of structure for system with facial artifacts rejection. All available techniques were divided into two main groups: based on reconstruction of respiration from 3-D image of object and based on 2-D image processing of techniques. Structure of system for respiration monitoring using optical sensors with facial artifacts removing was developed. New approach allows improving of respiration monitoring for objects in supine position and in a sitting position.

References 26, figures 16.

Article Details

How to Cite
Боділовський, О. К. (2014). Non-contact respiration monitoring using optical sensors. Electronics and Communications, 19(1), 37–46. https://doi.org/10.20535/2312-1807.2014.19.1.142302
Section
Biomedical devices and systems

References

K. R. Jones. (1982), “A respiration monitor foe use with CT body scanning and other imaging tech-niques,” Br. J. Radiol., vol. 55, no. 655, pp. 530–533, Jul.

R. Ehman, M. McNamara, M. Pallack, H. Hricak, and C. Higgins. (1984). “Magnetic resonance imaging with respiratory gating: techniques and advantages,” Am. J. Roentgenol., vol. 143, no. 6, pp. 1175–1182, Dec.

T. Li, J. Geng, and S. Li. (2013), “Automatic respiration tracking for radiotherapy using optical 3D camera,” pp. 861804–861804

A. K. Abbas, K. Heimann, K. Jergus, T. Orlikowsky, and S. Leonhardt. (2011), “Neonatal Non-contact Respiratory Monitoring based on Real-time Infrared Thermography,” Biomed. Eng. OnLine, vol. 10, no. 1, p. 93.

H. Aoki and K. Koshiji. (2007), “Non-contact Respiration Monitoring Method for Screening Sleep Res-piratory Disturbance Using Slit Light Pattern Projection,” in World Congress on Medical Physics and Biomedical Engineering 2006, R. Magjarevic and J. H. Nagel, Eds. Springer Berlin Heidelberg, Рp. 680–683.

N. A. Borghese and S. Ferrari. (2012), “3D Surface Reconstruction: Multi-Scale Hierarchical Approach-es”. Springer, 2012.

I. Sato and M. Nakajima. (2005), “Non-contact Breath Motion Monitor ing System in Full Automation,” in Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005. 27th Annual International Conference of the, Pp. 3448–3451.

H. Aoki, K. Koshiji, H. Nakamura, Y. Takemura, and M. Nakajima. (2005), “Study on respiration moni-toring method using near-infrared multiple slit-lights projection,” in 2005 IEEE International Symposi-um on Micro-NanoMechatronics and Human Science, Pp. 291–296.

K. Tamagawa, K. Ogawa, and M. Nakajima. (2002), “Detection of respiratory movement during SPECT/PET data acquisition,” in 2002 IEEE Nuclear Science Symposium Conference Record, vol. 3, pp. 1571–1574 vol.3.

K. Povšič, M. Fležar, J. Možina, and M. Jezeršek. (2012), “Laser 3-D measuring system and real-time visual feedback for teaching and correcting breathing,” J. Biomed. Opt., vol. 17, no. 3, p. 036004, Mar.

H. Aoki, M. Miyazaki, H. Nakamura, R. Furukawa, R. Sagawa, and H. Kawasaki. (2012), “Non-contact respiration measurement using structured light 3-D sensor,” in 2012 Proceedings of SICE Annual Con-ference (SICE), Pp. 614–618.

M. Martinez and R. Stiefelhagen. (2012), “Breath rate monitoring during sleep using near-ir imagery and PCA,” in 2012 21st International Conference on Pattern Recognition (ICPR), Pp. 3472–3475.

J. Xia and R. A. Siochi. (2012), “A real-time respiratory motion monitoring system using KINECT: Proof of concept,” Med. Phys., vol. 39, no. 5, pp. 2682–2685.

M.-C. Yu, H. Wu, J.-L. Liou, M.-S. Lee, and Y.-P. Hung. (2013), “Multiparameter Sleep Monitoring Us-ing a Depth Camera,” in Biomedical Engineering Systems and Technologies, J. Gabriel, J. Schier, S. V. Huffel, E. Conchon, C. Correia, A. Fred, and H. Gamboa, Eds. Springer Berlin Heidelberg, Pp. 311–325.

G. F. Lewis, R. G. Gatto, and S. W. Porges. (2011), “A novel method for extracting respiration rate and relative tidal volume from infrared thermography,” Psychophysiology, vol. 48, no. 7, pp. 877–887.

Y.-M. Kuo, J.-S. Lee, and P. Chung. (2010), “A Visual Context-Awareness-Based Sleeping-Respiration Measurement System,” IEEE Trans. Inf. Technol. Biomed., vol. 14, no. 2, pp. 255–265.

B. D. Lucas and T. Kanade. (1981), “An iterative image registration technique with an application to stereo vision.,” in IJCAI, vol. 81, pp. 674–679.

B. K. P. Horn and B. G. Schunck. (1981), “Determining optical flow,” Artif. Intell., vol. 17, no. 1–3, pp. 185–203.

J. C. S. Jacques, C. R. Jung, and S. Raupp Musse. (2005), “Background Subtraction and Shadow De-tection in Grayscale Video Sequences,” in 18th Brazilian Symposium on Computer Graphics and Im-age Processing, 2005. SIBGRAPI 2005, Pp. 189–196.

C. R. Jung. (2009), “Efficient Background Subtraction and Shadow Removal for Monochromatic Vid-eo Sequences,” IEEE Trans. Multimed., vol. 11, no. 3, pp. 571–577.

K. Nakajima, A. Osa, and H. Miike. (1997), “A method for measuring respiration and physical activity in bed by optical flow analysis,” in Proceedings of the 19th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol. 5, pp. 2054–2057 vol.5.

K. Nakajima, Y. Matsumoto, and T. Tamura. (2001), “Development of real-time image sequence analy-sis for evaluating posture change and respiratory rate of a subject in bed,” Physiol. Meas., vol. 22, no. 3, p. N21, Aug.

K. S. Tan, R. Saatchi, H. Elphick, and D. Burke. (2010), “Real-time vision based respiration monitoring system,” in 2010 7th International Symposium on Communication Systems Networks and Digital Sig-nal Processing (CSNDSP), Pp. 770–774.

Z. Weixing and W. Zhilei. (2010), “Detection of porcine respiration based on machine vision,” in 2010 3rd International Symposium on Knowledge Acquisition and Modeling (KAM), pp. 398–401.

B. Ji, F. Qin, and H. Liu. (2012), “An Expression of Animal Abdominal Breathing Based on Intercept,” in 2012 International Conference on Biomedical Engineering and Biotechnology (iCBEB), Pp. 716–719.

P. Viola and M. J. Jones. (2004), “Robust Real-Time Face Detection,” Int. J. Comput. Vis., vol. 57, no. 2, pp. 137–154, May.