Skip to main content
SpringerLink
Log in

Daily life behaviour monitoring for health assessment using machine learning: bridging the gap between domains

  • Original Article
  • Published:
Personal and Ubiquitous Computing Aims and scope Submit manuscript

Abstract

Analysis of human behaviour for deducing health and well-being information is one of the contemporary challenges given the ageing in place. To this end, existing and newly developed machine learning methods are needed to be evaluated using annotated real-world data sets. However, the metrics used in performance evaluation are directly taken from the machine learning domain, and they do not necessarily consider the specific needs of human behaviour analysis such as recognizing the duration, start time and frequency of the activities. Moreover, the commonly used metrics such as accuracy or F-measure can be misleading in the presence of skewed class distributions as in the case of human behaviour recognition. In this study, we evaluate the performance of two machine learning methods, hidden Markov model and time windowed neural network on five different real-world data sets through human behaviour understanding for health assessment perspective. According to the experimental results, standard metrics fail to reveal the actual performance of the two compared machine learning methods in terms of behaviour recognition. On the other hand, the proposed evaluation mechanism which considers three different activity categories leads to a more realistic evaluation of the overall performance.

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
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  1. Alemdar H, Ersoy C (2010) Wireless sensor networks for healthcare: a survey. Comput Netw 54(15):2688–2710

    Article  Google Scholar 

  2. Alemdar H, Ertan H, Incel OD, Ersoy C (2013) ARAS human activity datasets in multiple homes with multiple residents. In: 7th International Conference on pervasive computing technologies for healthcare. Venice, Italy

  3. Alvarez G, Ayas N (2004) The impact of daily sleep duration on health: a review of the literature. Prog Cardiovasc Nurs 19(2):56

    Article  Google Scholar 

  4. Bamis A, Lymberopoulos D, Teixeira T, Savvides A (2010) The behaviorscope framework for enabling ambient assisted living. Pers Ubiquitous Comput 14(6):473–487

    Article  Google Scholar 

  5. Cook DJ (2012) Learning setting-generalized activity models for smart spaces. IEEE Intell Syst 27(1):32–38

    Article  Google Scholar 

  6. Gaddam A, Mukhopadhyay S, Gupta G (2011) Elder care based on cognitive sensor network. IEEE Sensors J 11(3):574–581

    Article  Google Scholar 

  7. Gallissot M, Caelen J, Bonnefond N, Meillon B, Pons S (2011) Using the multicom domus dataset. Research Report RR-LIG-020, LIG, Grenoble, France

  8. Gangwisch JE, Heymsfield SB, Boden-Albala B, Buijs RM, Kreier F, Pickering TG, Rundle AG, Zammit GK, Malaspina D (2006) Short sleep duration as a risk factor for hypertension: analyses of the first National Health and Nutrition Examination Survey. Hypertension 47(5):833–839

    Article  Google Scholar 

  9. Ghasemzadeh H, Jafari R (2011) Physical movement monitoring using body sensor networks: a phonological approach to construct spatial decision trees. IEEE Trans Ind Inf 7(1):66–77

    Article  Google Scholar 

  10. Gordon D, Czerny J, Beigl M (2014) Activity recognition for creatures of habit. Pers Ubiquitous Comput 18(1):205–221

    Article  Google Scholar 

  11. Helal S, Mann W, El-Zabadani H, King J, Kaddoura Y, Jansen E (2005) The gator tech smart house: a programmable pervasive space. Computer 38(3):50–60

    Article  Google Scholar 

  12. Hong X, Nugent CD (2013) Segmenting sensor data for activity monitoring in smart environments. Person Ubiquitous Comput 17(3):545–559

    Article  Google Scholar 

  13. Hu J, Brown MK, Turin W (1996) Hmm based on-line handwriting recognition. IEEE Trans Pattern Anal Mach Intell 18(10):1039–1045

    Article  Google Scholar 

  14. Intille SS, Larson K, Beaudin JS, Nawyn J, Tapia EM, Kaushik P (2005) A living laboratory for the design and evaluation of ubiquitous computing technologies. In: Extended abstracts of the 2005 conference on human factors in computing systems, ACM Press, pp 1941–1944

  15. van Kasteren T, Englebienne G, Kröse B (2011) Human activity recognition from wireless sensor network data: benchmark and software. In: Activity recognition in pervasive intelligent environments, Springer, pp 165–186

  16. van Kasteren T, Noulas A, Englebienne G, Kröse B (2008) Accurate activity recognition in a home setting. Proceedings of the 10th international conference on Ubiquitous computing, UbiComp ’08, pp 1–9 ACM, New York, NY, USA

  17. Kientz JA, Patel SN, Jones B, Price E, Mynatt ED, Abowd GD (2008) The Georgia tech aware home. In: HI ’08 extended abstracts on human factors in computing systems

  18. Kuo CH, Chen CT, Chen TS, Kuo YC (2011) A wireless sensor network approach for rehabilitation data collections. In: Proceedings of 2011 IEEE international conference on systems, man, and cybernetics (SMC), pp 579–584

  19. Lasserre J, Bishop CM (2007) Generative or discriminative? Getting the best of both worlds. Bayesian Stat 8:3–24

    MathSciNet  Google Scholar 

  20. Rabiner LR (1989) A tutorial on Hidden Markov models and selected applications in speech recognition. Proc IEEE 77(2):257–286

    Article  Google Scholar 

  21. Salah AA, Gevers T, Sebe N, Vinciarelli A (2010) Challenges of human behavior understanding. Proceedings of the first international conference on human behavior understanding, HBU’10, Springer-Verlag, Berlin, Heidelberg, pp 1–12

  22. Suryadevara N, Mukhopadhyay S (2012) Wireless sensor network based home monitoring system for wellness determination of elderly. IEEE Sens J 12(6):1965–1972

    Article  Google Scholar 

  23. Tapia EM, Intille SS, Larson K (2004) Activity recognition in the home using simple and ubiquitous sensors. In: Proceedings of international conference on pervasive computing, pp 158–175

  24. Waibel A, Hanazawa T, Hinton G, Shikano K, Lang KJ (1989) Phoneme recognition using time-delay neural networks. IEEE Trans Acoust Speech Signal Process 37(3):328–339

    Article  Google Scholar 

  25. Ward J, Lukowicz P, Gellersen H (2011) Performance metrics for activity recognition. ACM Trans Inf Syst Technol (TIST) 2(1)

Download references

Acknowledgments

This work is supported by the Boğaziçi University Research Fund under the Grant Number 8684.

Author information

Authors and Affiliations

  1. Department of Computer Engineering, Boğaziçi University, Istanbul, Turkey

    Hande Alemdar, Can Tunca & Cem Ersoy

Authors
  1. Hande Alemdar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Can Tunca
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Cem Ersoy
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hande Alemdar.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Alemdar, H., Tunca, C. & Ersoy, C. Daily life behaviour monitoring for health assessment using machine learning: bridging the gap between domains. Pers Ubiquit Comput 19, 303–315 (2015). https://doi.org/10.1007/s00779-014-0823-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00779-014-0823-y

Keywords

Search

Navigation