Skip to main content

Towards a Language for Defining Human Behavior for Complex Activities

Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 1253)

Abstract

This paper outlines the steps for a specification to create a language for defining human behavior in the context of complex activities, with a specific focus on Activities of Daily Living (ADLs). The proposed specification to create a language for defining human behavior is used to develop a framework for representation of (1) macro-level tasks or actions associated with any complex activity, (2) the context parameters on which these tasks or actions are performed and (3) sequence of movements of various body parts associated with performing these actions on the context parameters in the context of the given complex activity. To evaluate the efficacy of this proposed work, it has been implemented on a dataset of ADLs. The results presented and discussed uphold the relevance for practical implementation of the proposed human behavior definition language in real-time settings for addressing various challenges and utilizing the full potential of activity centric computing for improving the quality of life and user experience during ADLs as well as for various other applications.

Keywords

  • Human-computer interaction
  • Activities of daily living
  • Big data
  • Human behavior
  • Smart homes
  • Smart cities

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-55307-4_47
  • Chapter length: 7 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   229.00
Price excludes VAT (USA)
  • ISBN: 978-3-030-55307-4
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   299.99
Price excludes VAT (USA)
Fig. 1.

References

  1. Lampropoulos, G., Siakas, K., Anastasiadis, T.: Internet of Things in the context of Industry 4.0: an overview. Int. J. Entrep. Knowl. 7(1), 4–19 (2019)

    CrossRef  Google Scholar 

  2. Davies, N., Siewiorek, D.P., Sukthankar, R.: Activity-based computing. Pervasive Comput. IEEE 7, 20–21 (2008)

    CrossRef  Google Scholar 

  3. Azkune, G., Almeida, A., López-de-Ipiña, D., Chen, L.: Extending knowledge driven activity models through data-driven learning techniques. Expert Syst. Appl. 42 (2015). https://doi.org/10.1016/j.eswa.2014.11.063

  4. Cheng, Z., Qin, L., Huang, Q., Jiang, S., Yan, S., Tian, Q.: Human group activity analysis with fusion of motion and appearance information. In: Proceedings of the 19th ACM International Conference on Multimedia, Scottsdale, Arizona, USA, pp. 1401–1404, 28 November–01 December 2011 (2011)

    Google Scholar 

  5. Skocir, P., Krivic, P., Tomeljak, M., Kusek, M., Jezic, G.: Activity detection in smart home environment. In: Proceedings of the 20th International Conference on Knowledge Based and Intelligent Information and Engineering Systems, 5–7 September 2016 (2016)

    Google Scholar 

  6. Doryab, A., Bardram, J.E.: Designing activity-aware recommender systems for operating rooms. In: Proceedings of the 2011 Workshop on Context-awareness in Retrieval and Recommendation, 13 February 2011 (2011)

    Google Scholar 

  7. Abascal, J., Bonail, B., Marco, A., Sevillano, J.L.: AmbienNet: an intelligent environment to support people with disabilities and elderly people. In: Proceedings of ASSETS 2008, Halifax, Nova Scotia, Canada, 13–15 October 2008 (2008)

    Google Scholar 

  8. Chan, M., Campo, E., Bourennane, W.¸ Bettahar, F., Charlon, Y.: Mobility behavior assessment using a smart-monitoring system to care for the elderly in a hospital environment. In: Proceedings of PETRA 2014, Island of Rhodes, Greece, 27–30 May 2014 (2014)

    Google Scholar 

  9. Yared, R., Abdulrazak, B., Tessier, T., Mabilleau, P.: Cooking risk analysis to enhance safety of elderly people in smart kitchen. In: Proceedings of PETRA 2015, Corfu, Greece, 01–03 July 2015 (2015)

    Google Scholar 

  10. Deen, M.J.: Information and communications technologies for elderly ubiquitous healthcare in a smart home. Pers. Ubiquitous Comput. 19, 573–599 (2015)

    CrossRef  Google Scholar 

  11. Civitarese, G., Bettini, C., Belfiore, S.: Let the objects tell what you are doing. In: Proceedings of Ubicomp/ISWC 2016 Adjunct, Heidelberg, Germany, 12–16 September 2016 (2016)

    Google Scholar 

  12. Iglesias, R., de Segura, N.G., Iturburu, M.: The elderly interacting with a digital agenda through an RFID pen and a touch screen. In: Proceedings of MSIADU 2009, Beijing, China, 23 October 2009 (2009)

    Google Scholar 

  13. Angelini, L., Nyffeler, N., Caon, M., Jean-Mairet, M., Carrino, S., Mugellini, E., Bergeron, L.: Designing a desirable smart bracelet for older adults. In: Proceedings of UbiComp 2013, Zurich, Switzerland, 8–12 September 2013 (2013)

    Google Scholar 

  14. Khosla, R., Chu, M.-T., Kachouie, R., Yamada, K., Yoshihiro, F., Yamaguchi, T.: Interactive multimodal social robot for improving quality of care of elderly in australian nursing homes. In: Proceedings of MM 2012, Nara, Japan, 29 October–2 November 2012 (2012)

    Google Scholar 

  15. Sarkar, D.P.: A nurse bot for elderly people. In: Proceedings of UbiComp/ISWC 2018 Adjunct, Singapore, 8–12 October 2018 (2018)

    Google Scholar 

  16. Thakur, N., Han, C.Y.: An improved approach for complex activity recognition in smart homes. In: Reuse in the Big Data Era. Lecture Notes in Computer Science, vol 11602, pp. 220–231. Springer, Cham (2019)

    Google Scholar 

  17. Thakur, N., Han, C.Y.: Framework for a personalized intelligent assistant to elderly people for activities of daily living. Int. J. Recent Trends Hum. Comput. Interact. (IJHCI) 9(1), 1–22 (2019)

    Google Scholar 

  18. Thakur, N., Han, C.Y.: Framework for an intelligent affect aware smart home environment for elderly people. Int. J. Recent Trends Hum. Comput. Interact. (IJHCI) 9(1), 23–43 (2019)

    Google Scholar 

  19. Thakur, N., Han, C.Y.: A context-driven complex activity framework for smart home. In: Proceedings of the 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON) 2018, Vancouver, Canada, 1–3 November 2018 (2018)

    Google Scholar 

  20. Thakur, N., Han, C.Y.: A hierarchical model for analyzing user experiences in affect aware systems. In: Proceedings of the 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON) 2018, Vancouver, Canada, 1–3 November 2018 (2018)

    Google Scholar 

  21. Thakur, N., Han, C.Y.: An approach to analyze the social acceptance of virtual assistants by elderly people. In: Proceedings of the 8th International Conference on the Internet of Things (IoT) 2018, Santa Barbara, California, 15–18 October 2018 (2018)

    Google Scholar 

  22. Thakur, N., Han, C.Y.: Methodology for forecasting user experience for smart and assisted living in affect aware systems. In: Proceedings of the 8th International Conference on the Internet of Things (IoT) 2018, Santa Barbara, California, 15–18 October 2018 (2018)

    Google Scholar 

  23. Thakur, N., Han, C.Y.: An activity analysis model for enhancing user experiences in affect aware systems. In: Proceedings of the IEEE 5G World Forum Conference (IEEE 5GWF) 2018, Santa Clara, California, 09–11 July 2018 (2018)

    Google Scholar 

  24. Thakur, N., Han, C.Y.: A virtual wisdom mining ‘pan’ for connecting retired experts with currently active professionals. In: Proceedings of IT Research Symposium, University of Cincinnati, 10 April 2018 (2018)

    Google Scholar 

  25. Thakur, N., Han, C.Y.: A complex activity based emotion recognition algorithm for affect aware systems. In: Proceedings of IEEE 8th Annual Computing and Communication Workshop and Conference (IEEE CCWC) 2018, Las Vegas, 08–10 January 2018 (2018)

    Google Scholar 

  26. Saguna, A.Z., Chakraborty, D.: Complex activity recognition using context driven activity theory and activity signatures. ACM Trans. Comput.-Hum. Interact. 20(6), Article 32 (2013)

    Google Scholar 

  27. Microsoft, Coordinate Spaces. https://msdn.microsoft.com/enus/library/hh973078.aspx

  28. Chakraborty, S., Han, C.Y., Zhou, X., Wee, W.G.: A context driven human activity recognition framework. In: International Conference on Health Informatics and Medical Systems HIMS 2016 (2016)

    Google Scholar 

  29. Ordóñez, F.J., de Toledo, P., Sanchis, A.: Activity recognition using hybrid generative/discriminative models on home environments using binary sensors. Sensors 13, 5460–5477 (2013)

    CrossRef  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Nirmalya Thakur .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Thakur, N., Han, C.Y. (2021). Towards a Language for Defining Human Behavior for Complex Activities. In: Ahram, T., Taiar, R., Langlois, K., Choplin, A. (eds) Human Interaction, Emerging Technologies and Future Applications III. IHIET 2020. Advances in Intelligent Systems and Computing, vol 1253. Springer, Cham. https://doi.org/10.1007/978-3-030-55307-4_47

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-55307-4_47

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-55306-7

  • Online ISBN: 978-3-030-55307-4

  • eBook Packages: EngineeringEngineering (R0)