Skip to main content

Elderly Safety: A Smartphone Based Real Time Approach

  • Conference paper

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 7910))

Abstract

As the number of elderly people living worldwide and average life expectancy increases, older adults’ safety assurance has become increasingly important. There are many works on safety issues of the elderly focusing on human activity classification. Most of them use external sensor devices and/or completely or partially user input based classification and prediction systems. In this paper, we have developed an algorithmic model, monitored and documented elderly people‘s daily activities by using the gyroscope and accelerometer of a smartphone and with the use of those data and model, we calculated how much activity is required or overdone for a subject in order to maintain a healthy lifestyle. More importantly, we built a real time system that could not only judge what basic activity the subject is currently doing, but also protect the subject from possible injury that might happen to the subject if abnormal data is received.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Okonji, E.: Nigeria: Global Mobile Subscription to Hit 9bn in 2017 (July 2012)

    Google Scholar 

  2. Centers for Medicare and Medicaid Services, Office of the Actuary, National Health Statistics Group, National Health Care Expenditures Data (January 2012)

    Google Scholar 

  3. Binder, S.: Injuries among older adults: the challenge of optimizing safety and minimizing unintended consequences, http://injuryprevention.bmj.com/content/8/suppl_4/iv2.full

  4. Long, X., Yin, B., Aarts, R.M.: Single-Accelerometer-Based Daily Physical Activity Classification. In: 31st Annual International Conference of the IEEE EMBS, Minneapolis, Minnesota, USA, September 2-6 (2009)

    Google Scholar 

  5. Ketabdar, H., Lyra, M.: System and Methodology for Using Mobile Phones in Live Remote Monitoring of Physical Activities. In: IEEE International Symposium on Technology and Society (2010)

    Google Scholar 

  6. WEKA: Open Source Data Mining Software, http://www.cs.waikato.ac.nz/ml/weka/

  7. Ainsworth, B.E., Haskell, W.L., Herrmann, S.D., Meckes, N., Bassett, D.R., Tudor-Locke, C., Greer, J.L., Vezina, J., et al.: Compendium of Physical Activities. Medicine & Science in Sports & Exercise 43(8), 1575–1581 (2011)

    Article  Google Scholar 

  8. Agrawal, R., Srikant, R.: Fast Algorithms for Mining Association Rules, http://rakesh.agrawal-family.com/papers/vldb94apriori.pdf

  9. Thiruvengada, H., Srinivasan, S., Gacic, A.: Design and Implementation of an Automated Human Activity Monitoring Application for Wearable Devices. IEEE (2008)

    Google Scholar 

  10. Bouten, C.V.C., Koekkoek, K.T.M., Verduin, M., Kodde, R., Janssen, J.D.: A Triaxial Accelerometer and Portable Data Processing Unit for the Assessment of Daily Physical Activity. IEEE Transactions on Biomedical Engineering 44(3) (1997)

    Google Scholar 

  11. Iyer, N., Bonissone, P.P.: Automated Risk Classification and outlier Detection. In: Proceedings of the 2007 IEEE Symposium on Computational Intelligence in Multicriteria Decision Making, MCDM 2007 (2007)

    Google Scholar 

  12. Yiping, T., Zhiying, Z., Hui, G., Huiqiang, L., Wei, W.: Elder Abnormal Activity Detection by Data Mining. In: SICE Annual Conference in Sapporo, August 4-6 (2004)

    Google Scholar 

  13. Abad, M.J.S., Sanchez-Iglesias, I., de Tella, A.: Evaluating Risk Propensity Using an Objective Instrument. The Spanish Journal of Psychology 14(1), 392–410 (2011)

    Article  Google Scholar 

  14. Nii, M., Nakai, K., Fujita, T., Takahashi, Y.: Action Estimation from Human Activity Monitoring Data using Soft Computing Approach. In: Third International Conference on Emerging Trends in Engineering and Technology. IEEE (2010)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Alam, M.A., Wang, W., Ahamed, S.I., Chu, W. (2013). Elderly Safety: A Smartphone Based Real Time Approach. In: Biswas, J., Kobayashi, H., Wong, L., Abdulrazak, B., Mokhtari, M. (eds) Inclusive Society: Health and Wellbeing in the Community, and Care at Home. ICOST 2013. Lecture Notes in Computer Science, vol 7910. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39470-6_17

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-39470-6_17

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-39469-0

  • Online ISBN: 978-3-642-39470-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics