Skip to main content

Advertisement

SpringerLink
Log in

Posture based health monitoring and unusual behavior recognition system for elderly using dynamic Bayesian network

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

Video surveillance cameras play a vital role in society. Elderly monitoring is one of the major applications of surveillance camera. It is observed that most of the elderly people live alone at homes. They desire aging at homes. Due to aging elderly people may experience some abnormal behaviors like chest pain, headache etc. Since they live alone these abnormal activities are unnoticed. This unnoticed activities cause severe health problems and finally may cause death. So a monitoring system is needed to monitor the behavior and give alerts to the care givers. A computer vision based elderly health care monitoring system using Dynamic Bayesian network (DBN) is developed. Modelling sequential data is an important feature in machine learning domain. The DBN model detects the abnormal activities such as backward fall, chest pain, forward fall, headache, and vomit. Human postures are recognized from silhouettes so that the privacy of the people is preserved and this model is robust to different environmental setup. This DBN model is both generative and discriminative and evaluated with real time video sequences and gives 82% accuracy. This system helps to give immediate attention to the people who are suffering in home alone due to severe health issues.

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
Fig. 6

Similar content being viewed by others

References

  1. Kleinberger, T., Becker, M., Ras, E., Holzinger, A., Müller, P.: Ambient intelligence in assisted living: enable elderly people to handle future interfaces. In: International Conference on Universal Access in Human-Computer Interaction. Springer, Berlin, pp. 103–112

    Chapter  Google Scholar 

  2. Storf, H., Kleinberger, T., Becker, M., Schmitt, M., Bomarius, F., Prueckner, S.: An eventdriven approach to activity recognition in ambient assisted living. In: European Conference on Ambient Intelligence, Springer, Berlin, 2009

    Google Scholar 

  3. Rafael Caba˜nas de Paz, M. Julia Flores, Jes´us Mart´ınez-G´omez, Dynamic Bayesian Network for Gesture Recognition, Xii Workshop De Agentes F´Isicos, Septiembre 2011, Albacete

  4. Edwards, J.: Wireless sensors relay medical insight to patients and caregivers [special reports]. IEEE Signal Process. Mag. 29(3), 8–12 (2012)

    Article  Google Scholar 

  5. Malhi, K., Mukhopadhyay, S.C., Schnepper, J., Haefke, M., Ewald, H.: A Zigbee-based wearable physiological parameters monitoring system. IEEE Sens. J. 12(3), 423–430 (2012)

    Article  Google Scholar 

  6. Mariani, B., Jiménez, M.C., Vingerhoets, F.J.G., Aminian, K.: On-shoe wearable sensors for gait and turning assessment of patients with Parkinson’s disease. IEEE Trans. Biomed. Eng. 60(1), 155–158 (2013)

    Article  Google Scholar 

  7. Chen, B.-R., et al.: A web-based system for home monitoring of patients with Parkinson’s disease using wearable sensors. IEEE Trans. Biomed. Eng. 58(3), 831–836 (2011)

    Article  Google Scholar 

  8. Mihailidis, A., Fernie, G.R., Cleghorn, W.L.: The development of a computerized cueing device to help people with dementia to be more independent. Technol. Disabil. 13(1), 23–40 (2000)

    Article  Google Scholar 

  9. Adlam, D., Gibbs, C., Orpwood, R.: The Gloucester smart house bath monitor for people with dementia. Phys. Med. Congr. Med. Phys. Clin. Eng. 17, 189 (2001)

    Google Scholar 

  10. Aziz, O., Robinovitch, S.N.: An analysis of the accuracy of wearable sensors for classifying the causes of falls in humans. IEEE Trans. Neural Syst. Rehabil. Eng. 19(6), 670–676 (2011)

    Article  Google Scholar 

  11. Ranhotigmage, C.: Human activities and posture recognition: innovative algorithm for highly accurate detection rate, 2013. http://mro.massey.ac.nz/handle/10179/4339

  12. Ma, D., Saxena, N., Xiang, T., Zhu, Y.: Location-aware and safer cards: enhancing RFID security and privacy via location sensing. IEEE Trans. Dependable Secur. Comput. 10(2), 57–69 (2013)

    Article  Google Scholar 

  13. Mukhopadhyay, S.C.: Wearable sensors for human activity monitoring—a review. IEEE Sens. J. 15(3), 1321–1330 (2015)

    Article  Google Scholar 

  14. Collins, R.T., Lipton, A.J., Kanade, T.: Introduction to the special section on video surveillance. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 745–746 (2000)

    Article  Google Scholar 

  15. Jalal, A., Kamal, S., Kim, D.: A depth video sensor-based life-logging human activity recognition system for elderly care in smart indoor environments. Sensors 14(7), 11735–11759 (2014)

    Article  Google Scholar 

  16. Brulin, D., Benezeth, Y., Courtial, E.: Posture recognition based on fuzzy logic for home monitoring of the elderly. IEEE Trans. Inf. Technol. Biomed. 16(5), 974–982 (2012)

    Article  Google Scholar 

  17. Wang, C.W., Hunter, A., Gravill, N., Matusiewicz, S.: Unconstrained video monitoring of breathing behavior and application to diagnosis of sleep apnea. IEEE Trans. Biomed. Eng. 61(2), 396–404 (2014)

    Article  Google Scholar 

  18. Khan, Z.A., Sohn, W.: Abnormal human activity recognition system based on R-Transform and kernel discriminant technique for elderly home care. IEEE Trans. Consum. Electron. 5(4), 1843–1850 (2011)

    Article  Google Scholar 

  19. Tabbone, S., Wendling, L., Salmon, J.P.: A new shape descriptor defined on the radon transform. Comput. Vis. Image Understand. J. 102(1), 42–51 (2006)

    Article  Google Scholar 

  20. Murphy, K.: Dynamic Bayesian Networks: Representation, Inference and Learning. University of California, Berkeley (2002)

    Google Scholar 

  21. Murphy, K.: The bayes net toolbox for matlab. Comput. Sci. Stat. 33(2), 1024–1034 (2001)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Information Technology, Rajalakshmi Engineering College, Chennai, India

    G. Anitha & S. Baghavathi Priya

Authors
  1. G. Anitha
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. S. Baghavathi Priya
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to G. Anitha.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Anitha, G., Baghavathi Priya, S. Posture based health monitoring and unusual behavior recognition system for elderly using dynamic Bayesian network. Cluster Comput 22 (Suppl 6), 13583–13590 (2019). https://doi.org/10.1007/s10586-018-2010-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-018-2010-9

Keywords

Search

Navigation