Advertisement

A Model New for Smart Home Technologies Knee Monitor and Walking Analyser

  • Kashif Nisar
  • Ag Asri Ag Ibrahim
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 739)

Abstract

Over the past century, in the most developed and rapidly developing countries, there has been a continuous increase in life expectancy primarily due to improvements in public health, nutrition, and medicine. However, this is now in parallel with aging population demographics and falling birth rates, which when combined, are expected to significantly burden the socio-economic well-being of many of these countries. In fact, never in human history have we been confronted with such a large aging population, nor have we developed solid, cost-effective solutions their healthcare and social needs, as well as the well-being of the elderly. In this paper, we will describe an ongoing project in Ubiquitous (U)-Healthcare - a smart medical home. In our research work, we are using advances in Information Technology (IT), wireless communication, web-based technologies, and autonomics, to develop new, smart, and cost-effective solutions for the health wellness of the elderly. Such a solution would enable the elderly to lead independent lifestyles in their own homes while being continuously, non-invasively, and non-intrusively monitored for the early detection of symptoms, so diseases can be treated in the early stages; to promote health wellness; as well as to treat chronic illnesses. In this research paper, through a few examples, we will discuss our ongoing work and the challenges we have uncovered, plus some of the research issues we are pursuing. We will particularly focus on the critical role of IT in developing innovative, low-cost, and high impacting solutions to the pending elderly demographic crisis. Several examples will be given to highlight IT for U-Health.

Keywords

Ubiquitous IT U-Health 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    N. Agoulmine, M.J. Deen, J-S. Lee and M. Meyyappan,: “U-Health Smart Home,” IEEE Nanotechnology Magazine, Vol. 5, Issue 3, pp. 6-11, September (2011).Google Scholar
  2. 2.
    Frost & Sullivan - 360 Degree CEO Perspective Of The Global Healthcare Industry (2008).Google Scholar
  3. 3.
    Gerard Anderson, and James JR. Knickman,: “Changing the Chronic care system to Meet People’s Needs,” Health Affairs, vol. 20, no. 6 (November/December 2001).Google Scholar
  4. 4.
    S. Mulroy, J. Gronley, W. Weiss, C. Newsam, J. Perry,: “Use of cluster analysis for gait pattern classification of patients in the early and late recovery phases following stroke,” Gait and Posture, vol. 18, pp114-125 (2003).Google Scholar
  5. 5.
    M. De Carlo and B. Armstrong,: “Rehabilitation of the knee following sports injury,” Clin Sports Med. vol. 29, no. 1, pp. 81-106 (2010).Google Scholar
  6. 6.
    R. Norton, A. J. Campbell, T. Lee-Joe, E. Robinson, M. Butler,: “Circumstances of falls resulting in hip fractures among older people,” J Am Geriatr Soc., vol. 45, pp. 1108–12 (1997).Google Scholar
  7. 7.
    Y. Makihara, H. Mannami, Y. Yagi,: “Gait Analysis of Gender and Age Using a Large-Scale Multi-View Gait Database,” Computer Vision – ACCV 2010: Lecture Notes in Comp Sci, vol. 6493, pp.440-51, Springer Verag (2011).Google Scholar
  8. 8.
    S. Handri, K. Nakamura, S. Nomura,: “Gender and Age Classification Based on Pattern of Human Motion Using Choquet Integral Agent Networks,” Journal of Advanced Computational Intelligence & Intelligent Informatics, vol.13, no.4 pp. 481-488 (2009).Google Scholar
  9. 9.
    B. Jina, T. Thua, E. Baek, S. Sakong, J. Xiao, M. J. Deen and T. Mondal,: “Walking-age analyser for healthcare applications”, unpublished (2012).Google Scholar
  10. 10.
    M. Loës, L. J. Dahlstedt, R. Thomée,: “A 7-year study on risks and costs of knee injuries in male and female youth participants in 12 sports,”, Scandinavian Jour of Medicine & Science in Sports, vol. 10, is 2, pp 90–97 (Ap 2000).Google Scholar
  11. 11.
    E. Monterio, S. Petryschuk, J. Wellstood, and M.J. Deen,: “Physiotherapy knee brace”, Project Report, McMaster University (2011).Google Scholar
  12. 12.
    C. Cali, C. Kiel, D. Kiel,: “An epidemiologic study of fall-related fractures among institutionalized older people,“ J Am Geriatr Soc vol. 43, pp. 1336–40 (1995).Google Scholar
  13. 13.
    S. Lustig et al.,: “The KneeKG system: a review of the literature”, Knee Surgery, Sports Traumatology, Arthroscopy, vol. 20, no. 4, pp. 633-638 (2012).Google Scholar
  14. 14.
    M. Sekine, T. Tamura, M. Akay, T. Fujimoto, T. Togawa, Y. Fukui,: “Discrimination of walking patterns using wavelet-based fractal analysis,” IEEE Trans. Neural Systems & Rehabilitation, vol. 10, no. 3, pp. 188-196 (Sep. 2002).Google Scholar
  15. 15.
    O. Perrin, P. Terrier, Q. Ladetto, B. Merminod, Y. Schutz,: “Improvement of walking speed prediction by accelerometry and altimetry, validated by satellite positioning,” Medical and Biological Engineering and Computing, vol. 38, no 2, pp. 164-168 (2000).Google Scholar
  16. 16.
    R. Ibrahim, E. Ambikairajah, B. Celler, N. Lovell,: “Time-Frequency based features for classification of walking patterns,” IEEE 15th Int. Conf. on Digital Signal Processing, pp. 187-190 (2007).Google Scholar
  17. 17.
    H. Menz, S. Lord, R. Fitzpatrick,: “Age-related differences in walking stability,” Age and Aging, vol.32, no.2, pp.137-142 (2003).Google Scholar
  18. 18.
    R. Ibrahim, E. Ambikairajah, B. Celler, N. Lovell,: “Gait Pattern Classification Using Compact Features Extracted From Intrinsic Mode Functions,” 30th Annual International IEEE Engineering in Medicine and Biology Society (EMBS 2008), pp.3852-3855, (August 2008).Google Scholar
  19. 19.
    M. Chen, J. Yan, Y. Xu,: “Gait pattern classification with integrated shoes,” IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2009), pp.833-839, Oct. 2009Google Scholar
  20. 20.
    M. Kubo, B. Ulrich,: “Coordination of pelvis-HAT (head, arms and trunk) in anterior-posterior and medio-lateral directions during treadmill gait in preadolescents with/without Down syndrome”, Gait and Posture, vol.23, no. 4, pp.512-518 (June 2006).Google Scholar
  21. 21.
    J. Wu, J. Wang, L. Liu,: “Kernel-Based Method for Automated Walking Patterns Recognition Using Kinematics Data”, Advances in Natural Computation - Lecture Notes in Computer Science, vol.4222, pp. 560-569, Springer Verlag (2006).Google Scholar
  22. 22.
    Z. Yang, L. Yang,: “A new definition of the intrinsic mode function”, World Academy of Science, Engineering and Technology, vol. 60, # page (2009).Google Scholar
  23. 23.
    N. Huang, Z. Shen, S. Long, M. Wu, H. Shih, Q. Zheng, N.-C. Yen, C. Tung, H. Liu,: “The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis,” Proceedings of the Royal Society of London A, vol. 454, no. 1971, pp. 903-995 (1998).Google Scholar
  24. 24.
    C. Bishop,: “Pattern Recognition and Machine Learning”, Science and Business Media, Springer (2006).Google Scholar
  25. 25.
    T. Kanungo, D. Mount, N. Netanyahu, C. Piatko, R. Silverman, A. Wu, “An Efficient k-Means Clustering Algorithm: Analysis and Implementation”, IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 24, no. 7, pp. 881-892, 2002.Google Scholar
  26. 26.
    S. Stancin, S. Tomazic, “Angle estimation of simultaneous orthogonal rotations from 3D gyroscope measurements”, Sensors, vol. 11, pp. 8536-8549, 2011.Google Scholar
  27. 27.
    A. Bianchi, M. Mendez, S. Cerutti, “Processing of signals recorded through smart devices: sleep-quality assessment”, IEEE Trans. Information Technology in Biomedicine, vol. 14, pp. 741-747, 2010.Google Scholar
  28. 28.
    AASM, “The international classification of sleep disorders, revised diagnostic and coding manual”, 2001.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Faculty of Computing and InformaticsUniversity Malaysia SabahKota Kinabalu SabahMalaysia

Personalised recommendations