Skip to main content
SpringerLink
Log in

Trajectory Patterns Mining Towards Lifecare Provisioning

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Pervasiveness of location acquisition technologies such as GPS, GSM, and Wi-Fi open the doors to use these technologies for ease and advancement of the society. One of the most emerging uses of these technologies is the low cost yet effective way of tracking of moving objects for activity monitoring. Daily lives of humans consist of enormous outdoor/trajectory activities like visiting different places for conducting routine task (e.g., Office, Restaurant, and Sports Club etc.). These activities put a significant effect in regulation of human life (i.e. health care and life care). By analyzing these activity traces and directing an effective routine of accomplishment of tasks can sufficiently improve its impact on human life. In this paper, we propose Daily Activity Monitor and LifeCare Provisioner (DALP) which is a GPS based outdoor activities analysis system for user monitoring and lifecare provisioning. To achieve real time and accurate outcome in tracking movement activities, we have proposed an approach of Personal tracking using static trajectory locations. DALP tracks the complete movement activity of a user and shares it with practitioners/instructors for analysis and updated recommendations. To verify and validate the working of DALP, a proof of concept prototype has been implemented that reflects its complete working.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Notes

  1. This work is an extended version of [33].

References

  1. Absolute Fitness, Android Market, www.androidfitness.com.

  2. AllSport GPS, Android Market, http://www.androlib.com/android.application.com-trimble-outdoors-gpsapp-android-ztCp.aspx.

  3. Backpacker GPS Trails, Android Market, http://www.backpacker.com/android-app/destinations/14052.

  4. Becker, M., Werkman, E., Anastasopoulos, M., & Kleinberger, T. (2006). Approaching ambient intelligent home care systems. In Pervasive health conference and workshops, Innsbruck, Austria.

  5. Beer Gut Fitness, Android Market, http://www.androlib.com/android.application.com-loph-beergut-nqzB.aspx.

  6. Blanke, U., & Schiele, B. (2009). Daily routine recognition through activity spotting. In Proceedings of the 4th international symposium on location and context awareness, LoCA.

  7. Braga, R. B., & Martin, H. (2011). CAPTAIN: A Context-Aware system based on Personal TrAckINg. In Proceeding of the 17th international conference on distributed multimedia systems/DMS 2011, Florence, Italy: DMS.

  8. Calorie Counter by FatSecret, Android Market, http://www.androlib.com/android.application.com-fatsecret-android-jqpD.aspx.

  9. CardioTrainer+Racing, Android Market, http://www.androlib.com/android.application.com-wsl-cardiotrainer-ray_feature-itp.aspx.

  10. Doctor, R. (April 1999) Deriving the Haversine formula, In The Math Forum, http://www.movable-type.co.uk/scripts/latlong.html.

  11. Dr. Mohammed Yaseen, The era of smart devices. Asia-Pacific III 2009, http://www.connect-world.com/~cwiml/index.php/component/k2/item/2667-the-era-of-smart-devices

  12. Endomondo Sports Tracker, Android Market, http://www.androlib.com/android.application.com-endomondo-android-xAED.aspx.

  13. Fahim, M, Fatima, I., Lee, S., & Lee, Y. K. (Feb 19–22, 2012). Daily Life Activity Tracking Application for Smart Homes using Android Smartphone. In Proceeding of 4th International Conference on Advanced Communication Technology (ICACT’12), Phoenix Park, Pyeongchang, Korea.

  14. Fast Food Calorie Counter, Android Market, http://www.androlib.com/android.application.com-concretesoftware-caloriecounter_full-jqtC.aspx

  15. Feixiang, Z., & Coll, N. (2011). Mining ship spatial trajectory patterns from AIS database for maritime surveillance. In Proceeding of 2nd IEEE international conference on emergency management and management sciences (ICEMMS), Beijing, China.

  16. Google Developers, Google Maps Android API v2, November, 2013: https://developers.google.com/maps/documentation/android/start#getting_the_google_maps_android_api_v2.

  17. Haghighi, P. D., Zaslavsky, A., Krishnaswamy, S., & Gaber, M. M. (2009). Mobile data mining for intelligent healthcare support. In Proceeding of 42nd Hawaii international conference on system sciences, Big Island, HI.

  18. Industry and government talks. (February 11, 2010). Mobile sensing for traffic, environmental monitoring and emergency response. Moffett Field, CA: Department of Homeland Security.

  19. Jane Jack Collins, The importance of medication reminders, http://ezinearticles.com/?The-Importance-of-Medication-Reminders&id=5129852.

  20. Jansen, B., & Deklerek, R. (2006). Context-aware inactivity recognition for visual fall detection. In Pervasive health conference and workshops, Innsbruck, Austria.

  21. Johnson, D. D., & Ganskopp, D. C. (March 2008). GPS collar sampling frequency: Effects on measures of resource use. Rangeland Ecology and Management.

  22. Kim, H., & Jo, H. (2006). A context-aware traveler healthcare service (THS) system. In Pervasive Health Conference and Workshops. November–December, Innsbruck, Austria.

  23. Klepeis, N. E., Tsang, A. M., & Behar, J. V. (September 1995) Analysis of the national human activity pattern survey(NHAPS) respondents from a standpoint of exposure assessment.

  24. Konstantas, D., Jones, V., & Bults, R. (2002). MobiHealth-Innovative 2.5/3G Mobile Services and Applications for Healthcare. IST Mobile and wireless telecommunications Summit.

  25. Laerhoven, K., Benny, L., Ng J., Thiemjarus, J. S., et al. (2004). Medical healthcare monitoring with wearable and implantable sensors. In Proceeding of the international workshop on ubiquitous computing for pervasive healthcare applications ubiHealth.

  26. Leijidekkers, P., & Gay, V. (2005). Personal heart monitoring and rehabilitation system using smart phones. In Proceedings of the international conference on mobile business (ICMB’05), Copenhagen.

  27. Mccurdy, T., & Graham, S. E. (2003). Using human activity data in exposure models: Analysis of discriminating factors. Journal of Exposure Analysis and Environmental Epidemiology. 13, 294–317.

    Google Scholar 

  28. Mei, H., Widya, I., Halteren, A., & Erfianto, B. (2007). A flexible vital sign representation framework for mobile healthcare. In First international conference on pervasive computing technologies for healthcare, Innsbruck, Austria.

  29. Microsoft Geolife project, GeoLife GPS Trajectories, http://research.microsoft.com/en-us/downloads/b16d359d-d164-469e-9fd4-daa38f2b2e13/.

  30. Paul Wright (February 8 2008). Mobile century: using GPS mobile phones as traffic sensors. UC Berkeley, CA: CITRIS—ITS seminar.

  31. Press Release, Text message prescription reminders significantly improve patient adherence to oral diabetes medication. http://www.marketwatch.com (May 24 2012).

  32. Rubel, P., Fayn, J., Nollo, G., Assanelli, D., Li, B., Restier, L., et al. (2005). Toward personal eHealth in cardiology: Results from the EPI-MEDICS telemedicine project. Journal of Electrocardiology. 38, 100–106

    Google Scholar 

  33. Saleem, M. A., Iram , F., Khan, K. U., Lee, Y. K., & Sungyoung, L. (December, 2012) Trajectory based activity monitoring and healthcare provisioning. In 10th IEEE conference of Pervasive Intelligence and Computing, Changzhou, China.

  34. Saleem, M. A., Lee, Y. K., Lee, S. (2013). Dynamicity in social trends towards trajectory based location recommendation. In proceeding of ICOST 2013 (pp 86–93).

  35. Saleem, M. A., Nawaz, W., Lee, Y. K., Lee, S. (2013). Road segment partitioning towards anomalous trajectory detection for surveillance applications. In proceeding of IRI 2013 (pp 610–617).

  36. Vajirkar, P., Singh, S., & Lee, Y. (2003). Context-aware data mining framework for wireless medical application. Lecture Notes in Computer Science (LNCS), Vol. 2736, Springer.

  37. Wertheimer, A., & Santella, T. M. (2005). Medication compliance research: Still so far to go. The Journal of Applied Research in Clinical and Experimental Therapeutics. 3, 254–261.

  38. Yue, Y., Zhuang, Y., Li, Q., & Ma, Q. (2009). Mining time-dependent attractive areas and movement patterns from taxi trajectory data. In Proceeding of 17th international conference on geoinformatics, Fairfax, VA.

Download references

Acknowledgments

This work was supported by a grant from the NIPA (National IT Industry Promotion Agency) in 2012. (Global IT Talents Program).

Author information

Authors and Affiliations

  1. Department of Computer Engineering, KyungHee University, Seoul, South Korea

    Muhammad Aamir Saleem, Young-Koo Lee & Sungyoung Lee

Authors
  1. Muhammad Aamir Saleem
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Young-Koo Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sungyoung Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Muhammad Aamir Saleem.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Saleem, M.A., Lee, YK. & Lee, S. Trajectory Patterns Mining Towards Lifecare Provisioning. Wireless Pers Commun 76, 747–762 (2014). https://doi.org/10.1007/s11277-013-1549-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-013-1549-2

Keywords

Search

Navigation