Advertisement

Recognition of Patterns of Health Problems and Falls in the Elderly Using Data Mining

  • Bogdan Pogorelc
  • Matjaž Gams
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7441)

Abstract

We present a generalized data mining approach to the detection of health problems and falls in the elderly for the purpose of prolonging their autonomous living. The input for the data mining algorithm is the output of the motion-capture system. The approach is general since it uses a k-nearest-neighbor algorithm and dynamic time warping with the time series of all the measurable joint angles for the attributes instead of a more specific approach with medically defined attributes. Even though the presented approach is more general and can be used to differentiate other types of activities or health problems, it achieves very high classification accuracies, similar to the more specific approaches described in the literature.

Keywords

health problems activities falls elderly machine learning data mining 

References

  1. 1.
    Bourke, A.K., et al.: An optimum accelerometer configuration and simple algorithm for accurately detecting falls. In: Proc. BioMed., pp. 156–160 (2006)Google Scholar
  2. 2.
    Bourke, A.K., O’Brien, J.V., Lyons, G.M.: Evaluation of a threshold-based tri-axial accelerometer fall detection algorithm. Gait & Posture 26, 194–199 (2007)CrossRefGoogle Scholar
  3. 3.
    Confidence Consortium. Ubiquitous Care System to Support Independent Living, http://www.confidence-eu.org
  4. 4.
    Craik, R., Oatis, C.: Gait Analysis: Theory and Application. Mosby-Year Book (1995)Google Scholar
  5. 5.
    eMotion. Smart motion capture system, http://www.emotion3d.com/smart/smart.html
  6. 6.
    Itakura, F.: Minimum prediction residual principle applied to speech recognition. IEEE Transactions on Acoustics, Speech and Signal Processing 23(1), 67–72 (1975)CrossRefGoogle Scholar
  7. 7.
    Kaluža, B., Mirchevska, V., Dovgan, E., Luštrek, M., Gams, M.: An Agent-Based Approach to Care in Independent Living. In: de Ruyter, B., Wichert, R., Keyson, D.V., Markopoulos, P., Streitz, N., Divitini, M., Georgantas, N., Mana Gomez, A. (eds.) AmI 2010. LNCS, vol. 6439, pp. 177–186. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  8. 8.
    Keogh, E., Ratanamahatana, C.A.: Exact indexing of dynamic time warping. Knowl. Inf. Syst. 7(3), 358–386 (2005)CrossRefGoogle Scholar
  9. 9.
    Lakany, H.: Extracting a diagnostic gait signature. Patt. Recognition 41, 1627–1637 (2008)zbMATHCrossRefGoogle Scholar
  10. 10.
    Luštrek, M., Kaluža, B.: Fall detection and activity recognition with machine learning. Informatica 33, 2 (2009)Google Scholar
  11. 11.
    Miskelly, F.G.: Assistive technology in elderly care. Age and Ageing 30, 455–458 (2001)CrossRefGoogle Scholar
  12. 12.
    Moore, S.T., et al.: Long-term monitoring of gait in Parkinson’s disease. Gait Posture (2006)Google Scholar
  13. 13.
    Perolle, G., Fraisse, P., Mavros, M., Etxeberria, L.: Automatic fall detection and acivity monitoring for elderly. COOP-005935 – HEBE Cooperative Research Project- CRAFT. Luxembourg (2006)Google Scholar
  14. 14.
    Pogorelc, B., Bosnić, Z., Gams, M.: Automatic recognition of gait-related health problems in the elderly using machine learning. Multimed Tools Appl. (2011), doi:10.1007/s11042-011-0786-1Google Scholar
  15. 15.
    Ribarič, S., Rozman, J.: Sensors for measurement of tremor type joint movements. MIDEM 37(2), 98–104 (2007)Google Scholar
  16. 16.
    Sakoe, H., Chiba, S.: Dynamic programming algorithm optimization for spoken word recognition. IEEE Transactions on Acoustics, Speech and Signal Processing 26(1), 43–49 (1978)zbMATHCrossRefGoogle Scholar
  17. 17.
    Salvador, S., Chan, P.: Toward accurate dynamic time warping in linear time and space. Intell. Data Anal. 11(5), 561–580 (2007)Google Scholar
  18. 18.
    Strle, D., Kempe, V.: MEMS-based inertial systems. MIDEM 37(4), 199–209 (2007)Google Scholar
  19. 19.
    Toyne, S.: Ageing: Europe’s growing problem. BBC News, http://news.bbc.co.uk/2/hi/business/2248531.stm
  20. 20.
    Trontelj, J., et al.: Safety Margin at mammalian neuromuscular junction – an example of the significance of fine time measurements in neurobiology. MIDEM 38(3), 155–160 (2008)Google Scholar
  21. 21.
    Dovgan, E., Luštrek, M., Pogorelc, B., Gradišek, A., Burger, H., Gams, M.: Intelligent elderly-care prototype for fall and disease detection from sensor data. Zdrav. Vestn. 80, 824–831 (2011)Google Scholar
  22. 22.
    Strle, B., Mozina, M., Bratko, I.: Qualitative approximation to Dynamic TimeWarping similarity between time series data. In: Proceedings of the Workshop on Qualitative Reasoning (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Bogdan Pogorelc
    • 1
    • 2
    • 3
  • Matjaž Gams
    • 1
    • 2
    • 3
  1. 1.Department of Intelligent SystemsJožef Stefan InstituteLjubljanaSlovenia
  2. 2.Špica International d. o. o.Slovenia
  3. 3.Jozef Stefan International Postgraduate SchoolSlovenia

Personalised recommendations