Remote Respiration Rate Determination in Video Data - Vital Parameter Extraction based on Optical Flow and Principal Component Analysis

Christian Wiede, Julia Richter, Manu Manuel, Gangolf Hirtz

Abstract

Due to the steadily ageing society, the determination of vital parameters, such as the respiration rate, has come into focus of research in recent years. The respiration rate is an essential parameter to monitor a person’s health status. This study presents a robust method to remotely determine a person’s respiration rate with an RGB camera. In our approach, we detected four subregions on a person’s chest, tracked features over time with optical flow, applied a principal component analysis (PCA) and several frequency determination techniques. Furthermore, this method was evaluated in various recorded scenarios. Overall, the results show that this method is applicable in the field Ambient Assisted Living (AAL).

References

  1. Balakrishnan, G. (2014). Analyzing pulse from head motions in video. PhD thesis, Massachusetts Institute of Technology.
  2. Bartula, M., Tigges, T., and Muehlsteff, J. (2013). Camerabased system for contactless monitoring of respiration. In Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE, pages 2672-2675. IEEE.
  3. Frigola, M., Amat, J., and Pags, J. (2002). Vision based respiratory monitoring system. In Proceedings of the 10th Mediterranean Conference on Control and AutomationMED2002 Lisbon, Portugal.
  4. Horn, B. K. and Schunck, B. G. (1981). Determining optical flow. Artificial intelligence , 17(1-3):185-203.
  5. Jin Fei and Pavlidis, I. (2010). Thermistor at a Distance: Unobtrusive Measurement of Breathing. IEEE Transactions on Biomedical Engineering, 57(4):988-998.
  6. Koolen, N., Decroupet, O., Dereymaeker, A., Jansen, K., Vervisch, J., Matic, V., Vanrumste, B., Naulaers, G., Van Huffel, S., and De Vos, M. (2015). Automated Respiration Detection from Neonatal Video Data:. In Proceedings of the International Conference on Pattern Recognition Applications and Methods, pages 164-169. SCITEPRESS - Science and and Technology Publications.
  7. Li, M. H., Yadollahi, A., and Taati, B. (2014). A noncontact vision-based system for respiratory rate estimation. In Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE, pages 2119-2122. IEEE.
  8. Lim, S. H., Golkar, E., Rahni, A., and Ashrani, A. (2014). Respiratory motion tracking using the kinect camera. In Biomedical Engineering and Sciences (IECBES), 2014 IEEE Conference on, pages 797-800. IEEE.
  9. Lukac, T., Pucik, J., and Chrenko, L. (2014). Contactless recognition of respiration phases using web camera. In Radioelektronika (RADIOELEKTRONIKA), 2014 24th International Conference, pages 1-4. IEEE.
  10. Martinez, M. and Stiefelhagen, R. (2012). Breath rate monitoring during sleep using near-ir imagery and pca. In Pattern Recognition (ICPR), 2012 21st International Conference on, pages 3472-3475. IEEE.
  11. Meinel, L., Richter, J., Schmidt, R., Findeisen, M., and Hirtz, G. (2015). Opdemiva: An integrated assistance and information system for elderly with dementia. In Consumer Electronics (ICCE), 2015 IEEE International Conference on, pages 76-77.
  12. Nakajima, K., Matsumoto, Y., and Tamura, T. (2001). Development of real-time image sequence analysis for evaluating posture change and respiratory rate of a subject in bed. Physiological Measurement, 22(3):N21.
  13. Ostadabbas, S., Sebkhi, N., Zhang, M., Rahim, S., Anderson, L. J., Lee, F. E.-H., and Ghovanloo, M. (2015). A Vision-Based Respiration Monitoring System for Passive Airway Resistance Estimation. IEEE Transactions on Biomedical Engineering, pages 1-1.
  14. Pentland, A. and Horowitz, B. (1991). Recovery of nonrigid motion and structure. IEEE Transactions on Pattern Analysis and Machine Intelligence, 13(7):730-742.
  15. Poh, M.-Z., McDuff, D., and Picard, R. (2011). Advancements in noncontact, multiparameter physiological measurements using a webcam. Biomedical Engineering, IEEE Transactions on, 58(1):7-11.
  16. Sharma, S., Bhattacharyya, S., Mukherjee, J., Purkait, P. K., Biswas, A., and Deb, A. K. (2015). Automated detection of newborn sleep apnea using video monitoring system. In Advances in Pattern Recognition (ICAPR), 2015 Eighth International Conference on, pages 1-6. IEEE.
  17. Shi, J. and Tomasi, C. (1993). Good features to track. Technical report, Cornell University, Ithaca, NY, USA.
  18. Statistisches Bundesamt (2015). 13. koordinierte Bevöelkerungsvorausberechnung für Deutschland. https://www.destatis.de/bevoelkerungspyramide/. [Online; accessed 07-Septemper-2016].
  19. Tan, K. S., Saatchi, R., Elphick, H., and Burke, D. (2010). Real-time vision based respiration monitoring system. In Communication Systems Networks and Digital Signal Processing (CSNDSP), 2010 7th International Symposium on, pages 770-774. IEEE.
  20. Tarassenko, L., Villarroel, M., Guazzi, A., Jorge, J., Clifton, D. A., and Pugh, C. (2014). Non-contact videobased vital sign monitoring using ambient light and auto-regressive models. Physiological Measurement, 35(5):807-831.
  21. Tomasi, C. and Kanade, T. (1991). Detection and Tracking of Point Features. Technical report, Carnegie Mellon University.
  22. Viola, P. and Jones, M. J. (2004). Robust real-time face detection. International Journal of Computer Vision, 57(2):137-154.
  23. Wu, H.-Y., Rubinstein, M., Shih, E., Guttag, J., Durand, F., and Freeman, W. T. (2012). Eulerian video magnification for revealing subtle changes in the world. ACM Trans. Graph. (Proceedings SIGGRAPH 2012), 31(4).
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Paper Citation


in Harvard Style

Wiede C., Richter J., Manuel M. and Hirtz G. (2017). Remote Respiration Rate Determination in Video Data - Vital Parameter Extraction based on Optical Flow and Principal Component Analysis . In Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2017) ISBN 978-989-758-225-7, pages 326-333. DOI: 10.5220/0006095003260333


in Bibtex Style

@conference{visapp17,
author={Christian Wiede and Julia Richter and Manu Manuel and Gangolf Hirtz},
title={Remote Respiration Rate Determination in Video Data - Vital Parameter Extraction based on Optical Flow and Principal Component Analysis},
booktitle={Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2017)},
year={2017},
pages={326-333},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006095003260333},
isbn={978-989-758-225-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2017)
TI - Remote Respiration Rate Determination in Video Data - Vital Parameter Extraction based on Optical Flow and Principal Component Analysis
SN - 978-989-758-225-7
AU - Wiede C.
AU - Richter J.
AU - Manuel M.
AU - Hirtz G.
PY - 2017
SP - 326
EP - 333
DO - 10.5220/0006095003260333