Multimodal Analysis for Behavioural Recognition in Tele-assistance Applications

Sorin Soviany, Sorin Puscoci

Abstract

The paper proposes an approach for behavioural recognition in which the individual conditions are recognized using a multimodal analysis method. This approach is an extension of our previously defined multimodal analysis method for biometrics; in this case the target application is the accurate recognition of human behaviour in smart home environments, with main focus in the home tele-assistance integrated services for elderly people. The proposed multimodal analysis method uses a hierarchical approach for data classification together with a fusion rule to combine the matching scores for several behavioural patterns. The approach novelty is given by the hierarchical classification design which provides an optimal performance-cost trade-off for the behavioural recognition system. This optimization could be done at runtime in practical applications.

References

  1. EC 2012 Ageing Report. https://ec.europa.eu/digitalagenda/en/news/2012-ageing report-economic-andbudgetary-projections-27-eu member-states-2010- 2060.
  2. Boulos, MK, Castellot Lou, R, Anastasiou, et al.,2009 Connectivity for Healthcare and Well-Being Management: Examples from Six European Projects, Int J Environ Res Public Health. 2009 July; 6(7): 1947-1971.
  3. EC 2007 “Ageing well in the Information Society”, COM (2007) 332final, Bruxelles. http://www.capsil. org/files/Action%20Plan%20on%20.Information%20a nd%20Communication%20Technologi %20 and% 20 Ageing.pdf.
  4. Pu?coci, Sorin, 2012. Tele-assistance integrated services, In Telecommunications, Anul LV, nr. 2.
  5. Reem Al-Attas, Abdulsalam Yassine, Shervin Shirmohammadi, 2012. Tele-medical applications in home-based health care. In 2012 IEEE International Conference on Multimedia and Expo Workshops.
  6. Soviany, Sorin, Puscoci, Sorin, 2014 An Optimized Multimodal Biometric System with Hierachical Classifiers and Reduced Features. In IEEE International Symposium on Medical Measurements and Applications (MeMeA),
  7. Soviany, Sorin, Puscoci, Sorin, 2013. A Feature Correlation-based Fusion Method for Fingerprint and Palmprint Identification Systems, In The 4th IEEE International Conference on E-Health and Bioengineering - EHB 2013 Grigore T Popa University of Medicine and Pharmacy, Ia§i, Romania,
  8. Soviany, Sorin, Puscoci, Sorin, Mariana Jurian, 2012 A multi-level hierarchical biometric fusion model for medical applications security, In the 8th Annual International Conference on Computer Science and Information Systems (INFOS2012), Atena, Grecia,
  9. Rodrigo Cilla, Miguel A. Patricio, Jesus Garcia, Antonio Berlanga and Jose M. Molina, 2009 Recognizing Human Activities from Sensors Using Hidden Markov Models Constructed by Feature Selection Techniques, In Algorithms 2009, 2, 282-300; oi:10.3390/a2010282.
  10. Young-Seol Lee and Sung-Bae Cho 2011, Activity Recognition Using Hierarchical Hidden Markov Models on a Smartphone with 3D Accelerometer, In E. Corchado, M. Kurzynski, M. Wozniak (Eds.): HAIS 2011, Part I, LNAI 6678, pp. 460-467, 2011. © Springer-Verlag Berlin Heidelberg 2011.
  11. B. Ugur Toreyin, E. Birey Soyer, Ibrahim Onaran, and A. Enis Cetin, 2008, Falling Person Detection UsingMultisensor Signal Processing, In Journal on Advances in Signal Processing Volume 2008, Article ID 149304,
  12. Nadia Zouba, Francois Bremond, Monique Thonnat. 2009, Multisensor Fusion for Monitoring Elderly Activities at Home. 6th IEEE International Conference on Advanced Video and Signal Based Surveillance AVSS09, Sep 2009, Genoa, Italy.
  13. Oliver Brdiczka, Matthieu Langet, Jérôme Maisonnasse, and James L. Crowley 2008, Detecting Human Behavior Models From Multimodal Observation in a Smart Home In IEEE Transactions on automation science and engineering, 2008.
  14. Rim Helaoui, Daniele Riboni, Mathias Niepert, Claudio Bettini, Heiner Stuckenschmidt, 2012, Towards Activity Recognition Using Probabilistic Description Logics, In Activity Context Representation: Techniques and Languages AAAI Technical Report WS-12-05.
Download


Paper Citation


in Harvard Style

Soviany S. and Puscoci S. (2015). Multimodal Analysis for Behavioural Recognition in Tele-assistance Applications . In Proceedings of the 1st International Conference on Information and Communication Technologies for Ageing Well and e-Health - Volume 1: ICT4AgeingWell, ISBN 978-989-758-102-1, pages 149-154. DOI: 10.5220/0005485901490154


in Bibtex Style

@conference{ict4ageingwell15,
author={Sorin Soviany and Sorin Puscoci},
title={Multimodal Analysis for Behavioural Recognition in Tele-assistance Applications},
booktitle={Proceedings of the 1st International Conference on Information and Communication Technologies for Ageing Well and e-Health - Volume 1: ICT4AgeingWell,},
year={2015},
pages={149-154},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005485901490154},
isbn={978-989-758-102-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 1st International Conference on Information and Communication Technologies for Ageing Well and e-Health - Volume 1: ICT4AgeingWell,
TI - Multimodal Analysis for Behavioural Recognition in Tele-assistance Applications
SN - 978-989-758-102-1
AU - Soviany S.
AU - Puscoci S.
PY - 2015
SP - 149
EP - 154
DO - 10.5220/0005485901490154