Identification of Gait Patterns Related to Health Problems of Elderly

  • Bogdan Pogorelc
  • Matjaž Gams
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6406)

Abstract

A system for automatic identification of gait patterns related to health problems of elderly for the purpose of supporting their independent living is proposed in this paper. The gait of the user is captured with the motion capture system, which consists of tags attached to the body and sensors situated in the apartment. Position of the tags is acquired by the sensors and the resulting time series of position coordinates are analyzed with machine learning algorithms in order to identify the specific health problem. We propose novel features for training a machine learning classifier that classifies the user’s gait into: i) normal, ii) with hemiplegia, iii) with Parkinson’s disease, iv) with pain in the back and v) with pain in the leg. Results show that naive Bayes needs more tags and less noise to reach classification accuracy of 98 % than random forest for 99 %.

Keywords

Health problems detection human motion analysis gait analysis machine learning data mining human locomotion ambient intelligence 

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References

  1. 1.
    Toyne, S.: Ageing: Europe’s growing problem. BBC News, http://news.bbc.co.uk/2/hi/business/2248531.stm 2009-01-19
  2. 2.
    Strle, D., Kempe, V.: MEMS-based inertial systems. Informacije MIDEM 37(4), 199–209 (2007)Google Scholar
  3. 3.
    Jurman, D., Jankovec, M., Kamnik, R., Topič, M.: Inertial and magnetic sensors: The calibration aspect. Informacije MIDEM 37(2), 67–72 (2007)Google Scholar
  4. 4.
    Dimic, F., Mušič, B., Osredkar, R.: An example of an integrated GPS and DR positioning system designed for archeological prospecting. Informacije MIDEM 38(2), 144–148 (2008)Google Scholar
  5. 5.
    Ribarič, S., Rozman, J.: Sensors for measurement of tremor type joint movements. Informacije MIDEM 37(2), 98–104 (2007)Google Scholar
  6. 6.
    Trontelj, J., Trontelj, J., Trontelj, L.: Safety Margin at mammalian neuromuscular junction – an example of the significance of fine time measurements in neurobiology. Informacije MIDEM 383(3), 155–160 (2008)Google Scholar
  7. 7.
    Bourke, A.K., Lyons, G.M.: A threshold-based fall-detection algorithm using a bi-axial gyroscope sensor. Medical Engineering & Physics 30(1), 84–90 (2006)CrossRefGoogle Scholar
  8. 8.
    Bourke, A.K., Scanaill, C.N., Culhane, K.M., O’Brien, J.V., Lyons, G.M.: An optimum accelerometer configuration and simple algorithm for accurately detecting falls. In: Proc. BioMed 2006, pp. 156–160 (2006)Google Scholar
  9. 9.
    Confidence: Ubiquitous Care System to Support Independent Living, http://www.confidence-eu.org
  10. 10.
    Craik, R., Oatis, C.: Gait Analysis: Theory and Application. Mosby-Year Book (1995)Google Scholar
  11. 11.
    Harrison, R.D.: Harrison’s principles of internal medicine, 14th edn. McGraw Hill, New York (1998)Google Scholar
  12. 12.
    Perry, J.: Gait Analysis: Normal and Pathological Function. McGraw-Hill, Inc., New York (1992)Google Scholar
  13. 13.
    eMotion. Smart motion capture system, http://www.emotion3d.com/smart/smart.html
  14. 14.
    Kangas, M., Konttila, A., Lindgren, P., Winblad, P., Jamsa, T.: Comparison of low-complexity fall detection algorithms for body attached accelerometers. Gait & Posture 28(2), 285–291 (2008)CrossRefGoogle Scholar
  15. 15.
    Lakany, H.: Extracting a diagnostic gait signature. Pattern recognition 41, 1627–1637 (2008)CrossRefMATHGoogle Scholar
  16. 16.
    Luštrek, M., Kaluža, B.: Fall Detection and Activity Recognition with Machine Learning. Informatica (Slovenia) 33(2), 197–204 (2009)Google Scholar
  17. 17.
    Maybeck, P.S.: Stochastic models, estimation, and control. Mathematics in Science and Engineering 141 (1979)Google Scholar
  18. 18.
    Qian, G., Guo, F., Ingalls, T., Olson, L., James, J., Rikakis, T.: A gesture-driven multimodal interactive dance system. In: Proc. ICME 2004, pp. 1579–1582 (2004)Google Scholar
  19. 19.
    Sukthankar, G., Sycara, K.: A cost minimization approach to human behavior recognition. In: Proc. AAMAS 2005, pp. 1067–1074 (2005)Google Scholar
  20. 20.
    Tapia, E.M., Intille, S.S., Haskell, W., Larson, K., Wright, J., King, A., Friedman, R.: Real-time recognition of physical activities and their intensities using wireless accelerometers and a heart rate monitor. In: Proc. ISWC 2007, pp. 37–40 (2007)Google Scholar
  21. 21.
    Vishwakarma, V., Mandal, C., Sura, S.: Automatic detection of human fall in video. In: Ghosh, A., De, R.K., Pal, S.K. (eds.) PReMI 2007. LNCS, vol. 4815, pp. 616–623. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  22. 22.
    Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)MATHGoogle Scholar
  23. 23.
    Zhang, T., Wang, J., Liu, P., Hou, J.: Fall detection by wearable sensor and One-Class SVM algorithm. LNCIS, vol. 345, pp. 858–863 (1988)Google Scholar
  24. 24.
    Zouba, N., Boulay, B., Bremond, F., Thonnat, M.: Monitoring activities of daily living (ADLs) of elderly based on 3D key human postures. In: Proc. ICVW 2008, pp. 37–50 (2008)Google Scholar
  25. 25.
    Moore, S.T., et al.: Long-term monitoring of gait in Parkinson’s disease. Gait Posture (2006) doi:10.1016/j.gaitpost.2006.09.011Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Bogdan Pogorelc
    • 1
    • 2
  • Matjaž Gams
    • 1
    • 2
  1. 1.Department of Intelligent SystemsJožef Stefan InstituteLjubljanaSlovenia
  2. 2.Špica International d. o. o.LjubljanaSlovenia

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