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Automatic Calculation of Body Mass Index Using Digital Image Processing

  • Juan D. J. Amador
  • Josué Espejel Cabrera
  • Jared Cervantes
  • Laura D. Jalili
  • José S. Ruiz Castilla
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 916)

Abstract

In this paper we present a vision system to detect BMI from images. The proposed system segments the image and extracts the most important features, from these features a classifier is trained. An analysis of the results with different classification techniques is presented in the experimental results. The results show that the system can obtain good classification accuracies using images under controlled conditions.

References

  1. 1.
    Bipemdu, H., Hayfron-Acquah, J.B., Panford, J.K., Appiah, O.: Calculation of body mass index using image processing techniques. Int. J. Artif. Intell. Mech. 4, 1 (2015)Google Scholar
  2. 2.
    Trefethen, N.: Calculate your new BMI. University of Oxford (2013)Google Scholar
  3. 3.
    Ares, G.: Mathematical and Statistical Methods in Food Science and Technology. Wiley, Hoboken (2013)Google Scholar
  4. 4.
    Mamat, M., Deraman, S.K., Noor, N.M.M., Zulkifli, N.F.: Relationship between body mass index and healthy food with a balanced diet. Appl. Math. Sci. 7(4), 153–159 (2013)Google Scholar
  5. 5.
    Wiam, B., Abdesslam, B., Mohamed, L., Mohamed, D.: A mathematical model of overweight/obesity in Morocco using human biomass. Int. J. Latest Res. Sci. Technol. 3(6), 65–67 (2014)Google Scholar
  6. 6.
    Duncan, M.J., Nevill, A., Woodfield, L., Al-Nakeeb, Y.: The relationship between pedometer-determined physical activity, body mass index and lean body mass index in children. Int. J. Pediatr. Obes. 5, 445–450 (2010)CrossRefGoogle Scholar
  7. 7.
    Pontaga, I., Zidens, J.: Estimation of body mass index in team sports athletes. LASE J. Sport Sci. 2, 33–44 (2011)Google Scholar
  8. 8.
    Franz, D.D., Feresu, S.A.: The relationship between physical activity, body mass index, and academic performance and college-age students. Open J. Epidemiol. 3, 4–11 (2013)CrossRefGoogle Scholar
  9. 9.
    Mwangi, F.M., Rintaugu, E.G.: Physical activity and health related physical fitness attributes of staff university members in a Kenyan Public University. Int. J. Sports Sci. 7(2), 81–86 (2017)Google Scholar
  10. 10.
    Karnyanszky, T.M., Musuri, C., Karnyanszky, C.A.: Expert software for determination of Juvenil’s people obesity. Annals Computer Sciencie Series 6: Tome 1 (2008)Google Scholar
  11. 11.
    Wen, L., Guo, G.: A computational approach to body mass index prediction from face images. Image Vis. Comput. 31(2013), 392–400 (2013)CrossRefGoogle Scholar
  12. 12.
    Mardolkar, M.: Body mass index (BMI) data analysis and classification. J. Comput. Sci. Inf. Technol. 6(2), 8–16 (2017)Google Scholar
  13. 13.
    Millard, L.A.C., Davies, N.M., Tilling, K., Gaunt, T.R., Smith, G.D.: Searching for the causal effects of BMI in over 300 000 individuals, using Mendelian randomization. bioRxiv preprint (2017). First posted online 19 Dec 2017Google Scholar
  14. 14.
    Madariaga, N.E., Linsangan, N.B.: Application of artificial neural network and background subtraction for determining BMI in Android devices using Bluetooth. Int. J. Eng. Technol. 8(5), 366 (2016)CrossRefGoogle Scholar
  15. 15.
    Larsen, B.S., Winther, S., Buttcher, M., Nissen, L., Struijk, J., Samuel, S.: Correlations of first and second heart sounds with age, sex, and body mass index. IEEE Comput. Cardiol., 4 (2017).  https://doi.org/10.22489/CinC.2017.141-408
  16. 16.
    Nahavandi, D., Abobakr, A., Haggag, H., Hossny, M., Nahavandi, S., Filippidis, D.: A skeleton-free kinect system for body mass index assessment using deep neural networks, pp. 1–6 (2017).  https://doi.org/10.1109/SysEng.2017.8088252
  17. 17.
    Borges, J., Bioucas, D.J., Maral, A.: Bayesian hyperspectral image segmentation with a discriminative class learning. IEEE Trans. Geosci. Remote Sens. 49(6), 2151–2164 (2011)CrossRefGoogle Scholar
  18. 18.
    Bernardo, J., Smith, A.: Bayesian Theory. Wiley, Hoboken (1994)CrossRefGoogle Scholar
  19. 19.
    Dempster, A., Laird, N., Rubin, D.: Maximum likelihood from incomplete data via the EM algorithm. J. R. Stat. Soc. 1(39), 1–38 (1977)MathSciNetzbMATHGoogle Scholar
  20. 20.
    Ng, A.Y., Jordan, M.I.: On discriminative vs. generative classifiers: a comparison of logistic regression and naive Bayes. In: Advances in Neural Information Processing Systems 14, pp. 841–848. MIT-Press (2002)Google Scholar
  21. 21.
    Russell, S., Norvig, P.: Artificial Intelligence: A Modern Approach, 2nd edn. Prentice Hall, Upper Saddle River (2003)zbMATHGoogle Scholar
  22. 22.
    Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagating errors. Nature 323(6088), 533–536 (1986)CrossRefGoogle Scholar
  23. 23.
    Werbos, P.J.: The Roots of Backpropagation. From Ordered Derivatives to Neural Networks and Political Forecasting. Wiley, New York (1994)Google Scholar
  24. 24.
    Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995).  https://doi.org/10.1007/978-1-4757-3264-1CrossRefzbMATHGoogle Scholar
  25. 25.
    Wang, Z.: Body mass index and all-cause mortality. JAMA 316(9), 991–992 (2016)CrossRefGoogle Scholar
  26. 26.
    Perera, S.: Body mass index is an important predictor for suicide: results from a systematic review and meta-analysis. Psychoneuroendocrinology 65, 76–83 (2016)CrossRefGoogle Scholar
  27. 27.
    Cohen, A., Baker, J., Ardern, C.I.: Association between body mass index, physical activity, and health-related quality of life in Canadian adults. Hum. Kinet. J. 24(1) (2016)CrossRefGoogle Scholar
  28. 28.
    Curtis, D.S., Fuller-Rowell, T.E., Doan, S.N., Zgierska, A.E., Ryff, C.D.: Racial and socioeconomic disparities in body mass index among college students: understanding the role of early life adversity. J. Behav. Med. 39(5), 866–875 (2016)CrossRefGoogle Scholar
  29. 29.
    D. Nahavandi, A. Abobakr?, H. Haggag, M. Hossny, S. Nahavandi and D. Filippidis A Skeleton-Free Kinect System for Body Mass Index Assessment using Deep Neural Networks. IEEEXPLORE (2016)Google Scholar
  30. 30.
    Sonka, M., Hlavac, V., Boyle, R.: Image Processing, Analysis and Machine Vision. Springer, Heidelberg (1993).  https://doi.org/10.1007/978-1-4899-3216-7CrossRefGoogle Scholar
  31. 31.
    Gonzalez, R.C., Woods, R.E.: Digital Image Processing Using MATLAB. Pearson, Upper Saddle River (2010)Google Scholar
  32. 32.
    Nixon, M., Aguado, A.: Feature Extraction and Image Processing. Academic Press, Cambridge (2002)Google Scholar
  33. 33.
    Zhang, S., Lei, Y.K.: Modified locally linear discriminant embedding for plant leaf recognition. Neurocomputing 74(14), 2284–2290 (2011)CrossRefGoogle Scholar
  34. 34.
    Hu, M.K.: Visual pattern recognition by moment invariants. IRE Trans. Inform. Theory 8, 179–187 (1962)zbMATHGoogle Scholar
  35. 35.
    Hu, R., Collomosse, J.: A performance evaluation of gradient field HOG descriptor for sketch based image retrieval. Comput. Vis. Image Underst. 117(7), 790–806 (2013)CrossRefGoogle Scholar
  36. 36.
    Flusser, J., Suk, T.: Pattern recognition by affine moment invariants. Pattern Recognit. 26(1), 167–174 (1993)MathSciNetCrossRefGoogle Scholar
  37. 37.
    Yang, M., Kpalma, K., Ronsin, J.: A Survey of Shape Feature Extraction Techniques, Pattern Recognition Techniques. INTECH Open Access Publisher (2008)Google Scholar
  38. 38.
    Liu, N., Kan, J.: Improved deep belief networks and multi-feature fusion for leaf identification. Neurocomputing 216, 460–467 (2016). ISSN 0925-2312CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Juan D. J. Amador
    • 1
  • Josué Espejel Cabrera
    • 1
  • Jared Cervantes
    • 2
  • Laura D. Jalili
    • 1
  • José S. Ruiz Castilla
    • 1
  1. 1.Posgrado e Investigación, UAEMEX (Autonomous University of Mexico State)TexcocoMexico
  2. 2.Instituto Politécnico NacionalEscuela Superior de Ingeniería Mecánica y EléctricaMexico CityMexico

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