Skip to main content
SpringerLink
Log in

Unsupervised Learning in Ambient Assisted Living for Pattern and Anomaly Detection: A Survey

  • Conference paper
Evolving Ambient Intelligence (AmI 2013)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 413))

Included in the following conference series:

Abstract

Population ageing is an issue that has encouraged the development of Ambient Intelligence systems to support elderly people to live autonomously at home longer. Some key aspects of these systems are the detection of behavior patterns and behavior profiles in their daily life. The information we can infer from these patterns could prove to be very valuable for monitoring the health status of a person, like to control deterioration of diseases or to provide personalized assistive services. In this paper we focus on the unsupervised learning techniques in health monitoring systems for elderly people, which has the advantage of not needing annotations. Collecting these is a tedious job and sometimes difficult to accomplish. We discuss the different existing approaches, identify some limitations and propose possible challenges and directions for future research.

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

Access this chapter

Log in via an institution
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

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Bloom, D.E., Canning, D., Fink, G.: Implications of population ageing for economic growth. Oxford Review of Economic Policy 26(4), 583–612 (2010)

    Article  Google Scholar 

  2. Aztiria, A., Izaguirre, A., Augusto, J.C.: Learning patterns in Ambient Intelligence environments: A Survey. Artificial Intelligence Review 34(1), 35–51 (2010)

    Article  Google Scholar 

  3. Mahoney, F.I., Barthel, D.: Functional evaluation: the Barthel Index. Maryland State Medical Journal 14, 56–61 (1965)

    Google Scholar 

  4. Bikakis, A., Patkos, T., Antoniou, G., Plexousakis, D.: A Survey of Semantics-based Approaches for Context Reasoning in Ambient Intelligence. In: Mühlhäuser, M., Ferscha, A., Aitenbichler, E. (eds.) AmI 2007 Workshops. CCIS, vol. 11, pp. 14–23. Springer, Heidelberg (2008)

    Google Scholar 

  5. Stumpp, J., Anastasopoulou, P., Sghir, H., Hey, S.: Sensor Chest Strap Wirelessly Coupled with an e-Diary for Ambulatory Assessment of Psycho-Physiological Data. In: 2nd Biennial Conference of the Science of Ambulatory Assessment, Ann Arbor, Michigan (2011)

    Google Scholar 

  6. Tolstikov, A., Biswas, J., Tham, C.K., Yap, P.: Eating activity primitives detection - a step towards adl recognition. In: 10th IEEE International Conference on e–Health Networking, Applications and Service, HEALTHCOM 2008, pp. 35–41 (2008)

    Google Scholar 

  7. Hoey, J., Poupart, J., Boutilier, C., Mihailidis, A.: Semi-supervised learning of a POMDP model of Patient-Caregiver Interactions. In: International Joint Conference in Artificial Intelligence, Workshop on Modeling Others from Observations, pp. 101–110 (2005)

    Google Scholar 

  8. Zhang, D., Gatica-Perez, D., Bengio, S.: Semi-supervised adapted HMMs for unusual event detection. In: Conference Computer Vision and Pattern Recognition, CVPR 2005, pp. 611–618 (2005)

    Google Scholar 

  9. Sandhu, J.S., Agogino, A.M., Agogino, A.K.: Wireless Sensor Networks for Commercial Lighting Control: Decision Making with Multi-agent Systems. In: Association for Advancement of Artificial Intelligence Conference, Sensor Networks Workshop, San Jose, pp. 88–92 (2004)

    Google Scholar 

  10. Monekosso, D.N., Remagnino, P.: Anomalous behaviour detection: supporting independent living. In: Monekosso, D.N., Remagnino, P., Kuno, Y. (eds.) Ambient Intelligence Techniques and Applications, Advanced Information and Knowledge Processing, pp. 33–48. Springer, London (2009)

    Google Scholar 

  11. Barger, T.S., Brown, D.E., Alwan, M.: Health-status monitoring through analysis of behavioral patterns. IEEE Transactions on SMC-A 35, 22–27 (2005)

    Google Scholar 

  12. Cook, D.J., Youngblood, M., Heierman III, E.O., Gopalratnam, K., Rao, S., Litvin, A., Khawaja, F.: MavHome: An Agent-Based Smart Home. In: First IEEE International Conference on Pervasive Computing and Communications, PerCom 2003, pp. 521–524 (2003)

    Google Scholar 

  13. Rao, S., Cook, D.J.: Predicting Inhabitant Actions Using Action and Task Models with Application to Smart Homes. International Journals of Artificial Intel. Tools 13(1), 81–100 (2004)

    Article  Google Scholar 

  14. Doctor, F., Hagras, H., Callaghan, V.: A fuzzy embedded agent-based approach for realizing ambient intelligence in intelligent inhabited environments. IEEE Transactions on Systems, Man, and Cybernetics, Part A 35(1), 55–65 (2005)

    Article  Google Scholar 

  15. Wyatt, D., Philipose, M., Choudhury, T.: Unsupervised activity recognition using automatically mined common sense. In: 20th National Conference on Artificial Intelligence, Pittsburgh, Pennsylvania, pp. 21–27 (2005)

    Google Scholar 

  16. Robben, S., Krose, B.: Longitudinal Residential Ambient Monitoring: Correlating Sensor Data to Functional Health Status. In: 7th International Conference on Pervasive Computing Technologies for Healthcare, PervasiveHealth, pp. 244–247 (2013)

    Google Scholar 

  17. Fisher, A.G.: Assessment of Motor and Process Skills, 6th edn. Development, Standardization, and Administration Manual, vol. 1. Three Star Press Inc., Fort Collins (2003)

    Google Scholar 

  18. Robben, S., Boot, M., Kanis, M., Kröse, B.: Identifying and Visualizing Relevant Deviations in Longitudinal Sensor Patterns for Care Professionals. In: 7th International Conference on Pervasive Computing Technologies for Healthcare, PervasiveHealth, pp. 416–419 (2013)

    Google Scholar 

  19. Virone, G., Sixsmith, A.: Monitoring activity patterns and trends of older adults. In: 30th IEEE Engineering in Medicine and Biology Society, Microtechnologies in Medicine & Biology, pp. 2071–2074 (2008)

    Google Scholar 

  20. Yin, G., Bruckner, D.: Daily activity model for ambient assisted living. In: Camarinha-Matos, L.M. (ed.) DoCEIS 2011. IFIP AICT, vol. 349, pp. 197–204. Springer, Heidelberg (2011)

    Google Scholar 

  21. Mozer, M.C.: Lessons from an Adaptive Home. In: Cook, D.J., Das, S.K. (eds.) Smart Environments: Technologies, Protocols, and Applications. John Wiley & Sons, Inc., Hoboken (2005)

    Google Scholar 

  22. Rivera-Illingworth, F., Callaghan, V., Hagras, H.A.: Neural Network Agent Based Approach to Activity Detection in AmI Environments. In: IEE International Workshop on Intelligent Environments, pp. 92–99 (2005)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. FZI Research Center for Information Technologies Embedded Systems and Sensors Engineering Karlsruhe, Germany

    Francisco Javier Parada Otte, Bruno Rosales Saurer & Wilhelm Stork

Authors
  1. Francisco Javier Parada Otte
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bruno Rosales Saurer
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wilhelm Stork
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Adaptive Information Cluster, School of Computer Science and Informatics, University College Dublin, Belfield, Dublin 4, Ireland

    Michael J. O’Grady

  2. University of Birjand, Iran

    Hamed Vahdat-Nejad

  3. Peter L. Reichertz Institute for Medical Informatics, Braunschweig, Germany

    Klaus-Hendrik Wolf

  4. Department of Computer Science, University College Dublin, Belfield, Dublin 4, Ireland

    Mauro Dragone

  5. University of St Andrews, UK

    Juan Ye

  6. RWTH Aachen University, Germany

    Carsten Röcker

  7. School of Computer Science and Informatics, University College Dublin, Belfield, Dublin 4, Ireland

    Gregory O’Hare

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer International Publishing

About this paper

Cite this paper

Parada Otte, F.J., Rosales Saurer, B., Stork, W. (2013). Unsupervised Learning in Ambient Assisted Living for Pattern and Anomaly Detection: A Survey. In: O’Grady, M.J., et al. Evolving Ambient Intelligence. AmI 2013. Communications in Computer and Information Science, vol 413. Springer, Cham. https://doi.org/10.1007/978-3-319-04406-4_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-04406-4_6

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-04405-7

  • Online ISBN: 978-3-319-04406-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Societies and partnerships

Search

Navigation