Skip to main content

Advertisement

SpringerLink
Log in

The behavioral profiling based on times series forecasting for smart homes assistance

  • Original Research
  • Published:
Journal of Ambient Intelligence and Humanized Computing Aims and scope Submit manuscript

Abstract

The many disadvantages of traditional assistance available to elderly and persons with cognitive dysfunction such as patients with Alzheimer’s disease have motivated the research of Technological assistance. The artificial agent, who will support the caregiver, is equipped with hardware and software resources that enable it to observe, analyze, infer and support, when needed, the assisted person. In this paper, we present the various stages of Technological assistance and propose a new algorithm for the step of activities models detection. We also explore an activity prediction step using time series. The experiments were conducted on real data recorded at LIARA smart home and the results are satisfactory.

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

Similar content being viewed by others

References

  • Agrawal R, Srikant R (1995) Mining sequential patterns. In: 11th international conference on data engineering (ICDE95). IEEE Computer Society, Taipei, Taiwan, pp 3–14

  • Alzheimer Society (2014) Rising tide: the impact of dementia on Canadian Society

  • Augusto JC, Nugent CD (2006) Smart homes can be smarter. Springer, Berlin

    Book  Google Scholar 

  • Bezdek JC, Ehrlich RW (1984) FCM: the fuzzy C-means clustering algorithm. Comput Geosci 10:191–203

    Article  Google Scholar 

  • Box GEP, Jenkins GM, Reinsel GC (1994) Time series analysis, forecasting and control, 3rd edn. Prentice Hall, Englewood Clifs

  • Chen L, Hoey J, Nugent C, Cook D (2012) Sensor-based activity recognition. IEEE Trans Syst Man Cybern Part C Appl Rev 42:790–808

    Article  Google Scholar 

  • Dupuis SL, Epp T, Smale B (2004) Caregivers of persons with dementia: roles, experiences. Supports and coping: A literature review, Ontario

  • Garg T, Malik A (2014) Survey on various enhanced K-means algorithms. Int J Adv Res Comput Commun Eng 3(11)

  • Jalal A, Jeong TK, Tae-Seong K (2011) Recognition of human home activities via depth silhouettes and transformation for smart homes. In: SHB2011—5th international symposium on sustainable healthy buildings, Seoul, Korea

  • Joseph BS (1984) Microcomputer based interactive analysis of univariate and multivariate ARIMA models. In: WSC 85 proceedings of the 17th conference on einter simulation

  • Jurek A, Nugent C, Bi Y, Wu S (2014) Clustering-based ensemble learning for activity recognition in smart homes. Special Issue of MDPI Sensors Journal for UCAm I & IWAAL 14:12285–12304

  • Kautkar R (2014) A comprehensive survey on data mining. Int J Res Eng Technol 3(8):185–191

  • Khim L, Shitan M (2002) The performance of AICC as an order selection criterion in ARMA time series models. Pertanika J Sci Technol 10:25–33

  • Kwiatkowski D, Phillips P, Schmidt P, Shin Y (1992) Testing the null hypothesis of stationarity against the alternative of a unit root. J Econom 54:159–178

    Article  MATH  Google Scholar 

  • Metras H (2005) RFID tags for ambient intelligence: present solutions and future challenges. In: Proceedings of the 2005 joint conference on Smart objects and ambient intelligence: innovative context-aware services: usages and technologies, ACM, New York, pp 43–46

  • Sadri F (2011) Ambient intelligence: a survey. ACM Comput 43:36–66

    Google Scholar 

  • Srinivasan R, Chen C, Cook DJ (2010) Activity recognition using Actigraph sensor. In: 4th international workshop on knowledge discovery from sensor data

  • Suryadevara N, Gaddam A, Rayudu R, Mukhopadhyay S (2011) Wireless sensors network based safe home to care elderly people: behaviour detection. Procedia Eng 25:96–99 (Elsevier)

    Article  Google Scholar 

  • Suryadevara N, Mukhopadhyay S, Wang R, Rayudu R (2013) Forecasting the behavior of an elderly using wireless sensors data in a smart home. Eng Appl Artif Intell 26:2641–2652 (Elsevier)

    Article  Google Scholar 

  • Van Tassel M (2011) Guidelines for increasing prompt efficiency in smart homes according to the residents profile and task characteristics. Springer, p 6719

Download references

Author information

Authors and Affiliations

  1. Department of Informatics, UQAC, Chicoutimi, Canada

    Mohamed Tarik Moutacalli, Abdenour Bouzouane & Bruno Bouchard

Authors
  1. Mohamed Tarik Moutacalli
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Abdenour Bouzouane
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bruno Bouchard
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohamed Tarik Moutacalli.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Moutacalli, M.T., Bouzouane, A. & Bouchard, B. The behavioral profiling based on times series forecasting for smart homes assistance. J Ambient Intell Human Comput 6, 647–659 (2015). https://doi.org/10.1007/s12652-015-0281-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12652-015-0281-z

Keywords

Search

Navigation