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Real-Time Classification of Lying Bodies by HOG Descriptors

  • A. Beltrán-Herrera
  • E. Vázquez-Santacruz
  • M. Gamboa-Zuñiga
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8495)

Abstract

In this paper we show a methodology for bodies classification in lying state using HOG descriptor and pressures sensors positioned in a matrix form (14 x 32 sensors) on the surface where bodies lie down. it will be done in real time. Due to current technology a limited number of sensors is used, wich results in low resolution data array, that will be used as image of 14 x 32 pixels. Our work considers the problem of human posture classification with few information (sensors), applying digital process to expand the original data of the sensors and so get more significant data for the classification, however, this is done with low-cost algorithms to ensure the real-time execution.

Keywords

Support Vector Machine Pressure Sensor Support Vector Machine Model Gradient Direction Image Descriptor 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Arcelus, A., Herry, C.L., Goubran, R.A., Knoefel, F., Sveistrup, H., Bilodeau, M.: Determination of sit-to-stand transfer duration using bed and floor pressure sequences. IEEE Trans. Biomed. Engineering 56(10), 2485–2492 (2009)CrossRefGoogle Scholar
  2. 2.
    Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-up robust features (surf). Comput. Vis. Image Underst. 110(3), 346–359 (2008)CrossRefGoogle Scholar
  3. 3.
    Byun, H., Lee, S.-W.: A survey on pattern recognition applications of support vector machines. International Journal of Pattern Recognition and Artificial Intelligence 17(3), 459–486 (2003)CrossRefGoogle Scholar
  4. 4.
    Chica, M., Campoy, P., Pérez, M.A., Rodríguez, T., Rodríguez, R., Valdemoros, Ó.: Corrigendum to “real-time recognition of patient intentions from sequences of pressure maps using artificial neural networks”. Computers in Biology and Medicine 43(9), 1302 (2013)CrossRefGoogle Scholar
  5. 5.
    Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)zbMATHGoogle Scholar
  6. 6.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 1, pp. 886–893 (2005)Google Scholar
  7. 7.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), vol. 1, pp. 886–893. IEEE Computer Society, Washington, DC (2005)CrossRefGoogle Scholar
  8. 8.
    De Vocht, J.W., Wilder, D.G., Bandstra, E.R., Spratt, K.F.: Biomechanical evaluation of four different mattresses. Applied Ergonomics 37(3), 297–304 (2006)CrossRefGoogle Scholar
  9. 9.
    Grimm, R., Bauer, S., Sukkau, J., Hornegger, J., Greiner, G.: Markerless estimation of patient orientation, posture and pose using range and pressure imaging. Int. J. Computer Assisted Radiology and Surgery 7(6), 921–929 (2012)CrossRefGoogle Scholar
  10. 10.
    Idzikowski, C.: Learn to Sleep Well. Watkins (2010)Google Scholar
  11. 11.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)CrossRefGoogle Scholar
  12. 12.
    Mannsfeld, S.C.B., Tee, B.C.-K., Stoltenberg, R.M., Chen, C.V.H.-H., Barman, S., Muir, B.V.O., Sokolov, A.N., Reese, C., Bao, Z.: Highly sensitive flexible pressure sensors with microstructured rubber dielectric layers. Nature Materials 9(10), 859–864 (2010)CrossRefGoogle Scholar
  13. 13.
    Manunza, I., Bonfiglio, A.: Pressure sensing using a completely flexible organic transistor. Biosensors and Bioelectronics 22(12), 2775–2779 (2007)CrossRefGoogle Scholar
  14. 14.
    Nicol, K., Rusteberg, D.: Pressure distribution on mattresses. Journal of Biomechanics 26(12), 1479–1486 (1993)CrossRefGoogle Scholar
  15. 15.
    Sekitani, T., Zschieschang, U., Klauk, H., Someya, T.: Flexible organic transistors and circuits with extreme bending stability. Nature Materials 9(12), 1015–1022 (2010)CrossRefGoogle Scholar
  16. 16.
    Seo, K.-H., Choi, T.-Y., Oh, C.: Development of a robotic system for the bed-ridden. Mechatronics 21(1), 227–238 (2011)CrossRefGoogle Scholar
  17. 17.
    Someya, T., Sekitani, T., Iba, S., Kato, Y., Kawaguchi, H., Sakurai, T.: A large-area, flexible pressure sensor matrix with organic field-effect transistors for artificial skin applications. Proceedings of the National Academy of Sciences of the United States of America 101(27), 9966–9970 (2004)CrossRefGoogle Scholar
  18. 18.
    Sensing Tex Smart Textiles (2013), Webpage: http://www.sensingtex.com/
  19. 19.
    Townsend, D., Holtzman, M., Goubran, R., Frize, M., Knoefel, F.: Relative thresholding with under-mattress pressure sensors to detect central apnea. IEEE Transactions on Instrumentation and Measurement 60(10), 3281–3289 (2011)CrossRefGoogle Scholar
  20. 20.
    Wang, J.-G., Li, J., Lee, C.Y., Yau, W.-Y.: Dense sift and gabor descriptors-based face representation with applications to gender recognition. In: ICARCV. IEEE (1860)Google Scholar
  21. 21.
    Hsu, C.W., Chang, C.C., Lin, C.J.: A practical guide to support vector classification (2010)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • A. Beltrán-Herrera
    • 1
  • E. Vázquez-Santacruz
    • 1
  • M. Gamboa-Zuñiga
    • 1
  1. 1.CGSTICCenter for Research and Advanced Studies of the National of Polytechnic Institute of Mexico (Cinvestav-IPN)México D.F.México

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