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A distributable event-oriented architecture for activity recognition in smart homes

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Abstract

In this paper, a new architecture is proposed for continuously generating, propagating, and delivering information by using event-based communication between independent agents. The resulting system can both handle heterogeneous smart environments and compute information in multiple places. With a communication method working as an abstraction layer, the proposed solution enables the use of multiple technologies at once. Additionally, different options for delivering the resulting data to client applications are explored. The implementation of this design as a platform written in Java with the Spring Framework is also presented, along with its handling of ten housing facilities equipped with various sensors (electromagnetic contacts, smart plugs, motion detectors, humidity, temperature, and light sensors). This paper is then concluded by an analysis of the platform workloads incurred by the tracking of a set of low-level activities. Finally, the code is distributed online for the benefit of the community.

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References

  1. Alberdi Aramendi A, Weakley A, Aztiria Goenaga A, Schmitter-Edgecombe M, Cook DJ (2018) Automatic assessment of functional health decline in older adults based on smart home data. J Biomed Inf 81:119–130

    Article  Google Scholar 

  2. Aminikhanghahi S, Cook DJ (2019) Enhancing activity recognition using cpd-based activity segmentation. Pervasive Mobile Comput 53:75–89

    Article  Google Scholar 

  3. Arthur J, Azadegan S (2005) Spring framework for rapid open source j2ee web application development: a case study. Sixth International Conference on Software Engineering. Artificial Intelligence, Networking and Parallel/Distributed Computing and First ACIS International Workshop on self-assembling wireless network, pp 90–95

  4. Augusto J, Giménez-Manuel J, Quinde M, Oguego C, Ali M, James-Reynolds C (2020) A smart environments architecture (search). Appl Artif Intell 34(2):155–186

    Article  Google Scholar 

  5. Augusto JC, Callaghan V, Cook D, Kameas A, Satoh I (2013) intelligent environments: a manifesto. Human Cent Comput Inf Sci 3(1):12

    Article  Google Scholar 

  6. Bainomugisha E, Carreton AL, Cutsem Tv, Mostinckx S, Meuter Wd (2013) A survey on reactive programming. ACM Comput Surv 45(4):1–34

    Article  Google Scholar 

  7. Bergeron F, Bouchard K, Gaboury S, Giroux S (2018) Tracking objects within a smart home. Expert Syst Appl 113:428–442

    Article  Google Scholar 

  8. Bota P, Silva J, Folgado D, Gamboa H (2019) A semi-automatic annotation approach for human activity recognition. Sensors 19:501

    Article  Google Scholar 

  9. Bouchard K, Bouchard B, Bouzouanea A (2014) Practical guidelines to build smart homes: lessons learned. Opportunistic networking, smart home, smart city, smart systems (book chapter), pp 1–37

  10. Bozdag E, Mesbah A, van Deursen A (2007) A comparison of push and pull techniques for ajax. In: 2007 9th IEEE International Workshop on web site evolution, pp 15–22

  11. Cigno RL, Russo A, Carra D (2008) On some fundamental properties of p2p push/pull protocols. In: 2008 Second International Conference on communications and electronics, pp 67–73

  12. Cook D, Crandall A, Thomas B, Krishnan N (2013) Casas: a smart home in a box. Computer 46:62–69

    Article  Google Scholar 

  13. Cook DJ (2009) Multi-agent smart environments. J Ambient Intell Smart Environ 1(1):51–55

    Article  Google Scholar 

  14. Cook DJ (2019) Learning setting-generalized activity models for smart spaces. IEEE Intell Syst 2010:1

    Google Scholar 

  15. Fielding RT, Taylor RN (2000) Architectural styles and the design of network-based software architectures. Ph.D. thesis, University of California, Irvine

  16. Fortin-Simard D et al (2015) Exploiting passive rfid technology for activity recognition in smart homes. IEEE Intell Syst 30(4):7–15

    Article  Google Scholar 

  17. Gutierrez F (2019) Web applications with spring boot. Apress, Berkeley, pp 87–125

    Google Scholar 

  18. Hao J, Bouzouane A, Gaboury S (2017) Complex behavioral pattern mining in non-intrusive sensor-based smart homes using an intelligent activity inference engine. J Reliab Intell Environ 3:99–116

    Article  Google Scholar 

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

    Article  Google Scholar 

  20. Hu Y et al (2016) Smart home in a box: usability study for a large scale self-installation of smart home technologies. J Reliab Intell Environ 2:93–106

    Article  Google Scholar 

  21. Kaewkasi C, Gurd JR (2008) Groovy aop: a dynamic aop system for a JVM-based language. In: Proceedings of the 2008 AOSD Workshop on software engineering properties of languages and aspect technologies, SPLAT ’08. ACM, New York, pp 3:1–3:6

  22. Klimczuk A (2012) Supporting the development of gerontechnology as part of silver economy building. J Interdiscip Res 2:52–56

    Google Scholar 

  23. Lavanya PG, Mallappa S (2019) Activity recognition from accelerometer data using symbolic data approach. In: Nagabhushan P, Guru DS, Shekar BH, Kumar YHS (eds) Data analytics and learning. Springer, Singapore, pp 317–329

    Chapter  Google Scholar 

  24. Levasseur M et al (2016) Identifying participation needs of people with acquired brain injury in the development of a collective community smart home. Disabil Rehabil Assistive Technol 11(8):636–644

    Article  Google Scholar 

  25. Lucassen G, Dalpiaz F, van der Werf JME, Brinkkemper S, Zowghi D (2017) Behavior-driven requirements traceability via automated acceptance tests. In: 2017 IEEE 25th International Requirements Engineering Conference Workshops (REW). IEEE, pp 431–434

  26. Lussier M et al (2019) Smart home technology: a new approach for performance measurements of activities of daily living and prediction of mild cognitive impairment in older adults. J Alzheimers Dis 68:85–96

    Article  Google Scholar 

  27. Lussier M et al (2020) Using ambient assisted living to monitor older adults with alzheimer’s disease: a single-case study to validate monitoring report. JMIR Med Inf

  28. Ma CY, Chen MH, Kira Z, AlRegib G (2019) Ts-lstm and temporal-inception: Exploiting spatiotemporal dynamics for activity recognition. Signal Process Image Commun 71:76–87

    Article  Google Scholar 

  29. Mshali H, Lemlouma T, Magoni D (2018) Adaptive monitoring system for e-health smart homes. Pervasive Mobile Comput 43:1–19

    Article  Google Scholar 

  30. Patel A, Shah J (2019) Sensor-based activity recognition in the context of ambient assisted living systems: a review. J Ambient Intell Smart Environ 11(4):301–322

    Article  Google Scholar 

  31. Plantevin V, Bouzouane A, Gaboury S (2017) The light node communication framework: A new way to communicate inside smart homes. Sensors 17(10):2397

    Article  Google Scholar 

  32. Redux website (2020). https://redux.js.org/introduction/getting-started. Accessed 07 May 2020

  33. Solis C, Wang X (2011) A study of the characteristics of behaviour driven development. In: 2011 37th EUROMICRO Conference on software engineering and advanced applications, pp 383–387

  34. de Sousa Santos I, de Castro Andrade RM, Rocha LS, Matalonga S, de Oliveira KM, Travassos GH (2017) Test case design for context-aware applications: are we there yet? Inf Softw Technol 88:1–16

    Article  Google Scholar 

  35. Spock Framework website (2019). http://spockframework.org. Accessed 22 May 2019

  36. Sprint G, Cook D, Fritz R, Schmitter-Edgecombe M (2016) Using smart homes to detect and analyze health events. Computer 49:29–37

    Article  Google Scholar 

  37. Stuart Russell PN (2011) Artificial intelligence: a mordern approach, chap 2, 3rd edn. Pearson, pp 34–63

  38. Tapia EM, Intille SS, Larson K (2004) Activity recognition in the home using simple and ubiquitous sensors. In: Ferscha A, Mattern F (eds) Pervasive Computing. Springer, Berlin, Heidelberg, pp 158–175

    Chapter  Google Scholar 

  39. United Nations DoE, Social Affairs PD (2017) World population ageing 2017. Tech. Rep. ST/ESA/SER.A/408, United Nations

  40. Valerio L, Passarella A, Conti M (2017) A communication efficient distributed learning framework for smart environments. Pervasive Mobile Comput 41:46–68

    Article  Google Scholar 

  41. Wang J, Chen Y, Hao S, Peng X, Hu L (2019) Deep learning for sensor-based activity recognition: A survey. Pattern Recogn Lett 119:3 – 11 (deep Learning for Pattern Recognition)

  42. Wielenga G (2015) Testing and code quality. Apress, Berkeley, pp 165–180

    Google Scholar 

  43. Wimo PA, et al (2015) World Alzheimer report 2015. Tech. rep., Alzheimer’s Disease International

  44. Yang Y et al (2019) Open-set human activity recognition based on micro-doppler signatures. Pattern Recogn 85:60–69

    Article  Google Scholar 

  45. Yao L et al (2018) Wits: an iot-endowed computational framework for activity recognition in personalized smart homes. Computing 100(4):369–385

    Article  MathSciNet  Google Scholar 

  46. Zheng Y, Kell S, Bulej L, Sun H, Binder W (2016) Comprehensive multiplatform dynamic program analysis for java and android. IEEE Softw 33(4):55–63

    Article  Google Scholar 

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Authors and Affiliations

  1. Laboratoire LIARA, Département d’informatique et de mathématique, Université du Québec à Chicoutimi, Chicoutimi, QC, Canada

    Cédric Demongivert, Kévin Bouchard, Sébastien Gaboury & Bruno Bouchard

  2. Faculty of Medicine, Research Center of Institut universitaire de gériatrie de Montréal, Université de Montréal, Montréal, QC, Canada

    Maxime Lussier & Nathalie Bier

  3. Laboratoire DOMUS, Département d’informatique, Université de Sherbrooke, Sherbrooke, QC, Canada

    Maxime Parenteau, Catherine Laliberté & Sylvain Giroux

  4. Integrated Health and Social Services University Network for West-Central Montreal, Côte-Saint-Luc, QC, Canada

    Mélanie Couture

  5. Department of psychology, Université de Sherbrooke, Côte-Saint-Luc, QC, Canada

    Mélanie Couture

Authors
  1. Cédric Demongivert
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  2. Kévin Bouchard
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  3. Sébastien Gaboury
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  4. Bruno Bouchard
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  5. Maxime Lussier
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  6. Maxime Parenteau
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  7. Catherine Laliberté
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  8. Mélanie Couture
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  9. Nathalie Bier
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  10. Sylvain Giroux
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Correspondence to Cédric Demongivert.

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Demongivert, C., Bouchard, K., Gaboury, S. et al. A distributable event-oriented architecture for activity recognition in smart homes. J Reliable Intell Environ 7, 215–231 (2021). https://doi.org/10.1007/s40860-020-00125-y

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  • DOI: https://doi.org/10.1007/s40860-020-00125-y

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