DOI QR코드

DOI QR Code

Level Set based Respiration Rate Estimation using Depth Camera

레벨 셋 기반의 깊이 카메라를 이용한 호흡수 측정

  • Oh, Kyeong Taek (Dept. of Medical Engineering, Yonsei University College of Medicine) ;
  • Shin, Cheung Soo (Dept. of Anesthesiology and Pain Medicine, Yonsei University College of Medicine) ;
  • Kim, Jeongmin (Dept. of Anesthesiology and Pain Medicine, Yonsei University College of Medicine) ;
  • Yoo, Sun Kook (Dept. of Medical Engineering, Yonsei University College of Medicine)
  • Received : 2017.04.25
  • Accepted : 2017.07.28
  • Published : 2017.09.30

Abstract

In this paper, we propose a method to measure respiration rate by dividing the respiration related region in depth image using level set method. In the conventional method, the respiration related region was separated using the pre-defined region designated by the user. We separate the respiration related region using level set method combining shape prior knowledge. Median filter and clipping are performed as a preprocessing method for noise reduction in the depth image. As a feasibility test, respiration activity was recorded using depth camera in various environments with arm movements or body movements during breathing. Respiration activity was also measured simultaneously using a chest belt to verify the accuracy of calculated respiration rate. Experimental results show that our proposed method shows good performance for respiration rate estimation in various situation compared with the conventional method.

Keywords

References

  1. F. Scopesi, M.G. Calevo, P. Rolfe, C. Arioni, C. Traggiai, F.M. Risso, et al., "Volume Targeted Ventilation (Volume Guarantee) in the Weaning Phase of Premature Newborn Infants," Pediatric Pulmonology, Vol. 42, Issue 10, pp. 864-870, 2007. https://doi.org/10.1002/ppul.20667
  2. H. Miwa and K. Sakai, "Development of Heart Rate and Respiration Rate Measurement System Using Body-sound," Proceedings of the 9th International Conference on Information Technology and Applications on Biomedicine, pp. 2168-2194, 2009.
  3. Y.Y. Nam, Y.S. Kim, and J.S. Lee, “Sleep Monitoring Based on a Tri-axial Accelerometer and a Pressure Sensor,” Sensors, Vol. 16, No. 5, pp. 750-764, 2016.
  4. M.C. Yu, J.L. Liou, S.W. Kuo, S.M. Lee, and Y.P. Hung, "Noncontact Respiratory Measurement of Volume Change Using Depth Camera," Proceeding of Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 2371-2374, 2012.
  5. A. Prochazka, M. Schatz, and M. Valis, “Mocrosoft Kinect Visual and Depth Sensors for Breathing and Heart Rate Analysis,” Sensors, Vol. 16, No. 7, pp. 996-1007, 2016.
  6. F. Benetazzo, A. Freddi, A. Monteriu, and S. Longhi, “Respiratory Rate Detection Algorithm Based On RGB-D Camera: Theoretical Background and Experimental Results,” Healthcare Technology Letters, Vol. 1, No. 3, pp. 81-86, 2014. https://doi.org/10.1049/htl.2014.0063
  7. M.S. Park and J.G. Kim, “Respiration Detection Method Using the PPG Signal Pattern,” Journal of Korea Multimedia Society, Vol. 19, No. 11, pp. 1862-1870, 2016. https://doi.org/10.9717/kmms.2016.19.11.1862
  8. D.T. Tin, D. Anh, H. Canh, T. Khoa, N.D. Huy, and M.V. Quan, "Measuring Human Respiration Rate using Depth Frames of Primesense Camera," International Conference on Advan ced Technologies for Communications, pp. 411-416, 2015.
  9. C. Arrieta, C. Sing-Long, S. Uribe, M.E. Andia, P.Irarrazaval, and C. Tejos, "Level Set Segmentation with Shape Prior Knowledge Using Intrinsic Rotation, Translation and Scaling Alignment," Proceeding of IEEE 12th International Symposium on Biomedical Imaging, pp. 1568-1571, 2015.
  10. How to Breath with Your Diaphragm, http://choirly.com/how-to-breathe-with-your-diaphragm (accessed Apr., 18, 2017).
  11. Epigastrium, https://en.wikipedia.org/wiki/Epigastrium (accessed Apr., 18, 2017).
  12. Muscles of Respiration, https://en.wikipedia.org/wiki/Muscles_of_respiration (accessed Apr., 18, 2017).
  13. T.F. Chan and L.A. Vese, “Active Contours without Edges,” IEEE Transactions on Image Processing, Vol. 10, No. 2, pp. 266-277, 2001. https://doi.org/10.1109/83.902291
  14. CREATIVE SENZ3D, http://asia.creative.com/p/web-cameras/creative-senz3d (accessed Apr., 15, 2017).
  15. J. Canny, “A Computational Approach To Edge Detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 8, No. 6, pp. 679-698, 1986.
  16. M.E. Lee, W.H. Cho, S.W. Kim, Y.J. Chen, and S.H. Kim, “Performance Comparison Between New Level Set Method and Previous Methods for Volume Image Segmentation,” Korea Information Processing Society, Vol. 188, No. 3, pp. 131-138, 2011.
  17. Pediatric Vital Signs Normal Ranges, https://iowaheadneckprotocols.oto.uiowa.edu/display/protocols/Pediatric+Vital+Signs+Normal+Ranges (accessed Apr., 15, 2017).