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WITS: an IoT-endowed computational framework for activity recognition in personalized smart homes

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Abstract

Over the past few years, activity recognition techniques have attracted unprecedented attentions. Along with the recent prevalence of pervasive e-Health in various applications such as smart homes, automatic activity recognition is being implemented increasingly for rehabilitation systems, chronic disease management, and monitoring the elderly for their personal well-being. In this paper, we present WITS, an end-to-end web-based in-home monitoring system for convenient and efficient care delivery. The system unifies the data- and knowledge-driven techniques to enable a real-time multi-level activity monitoring in a personalized smart home. The core components consist of a novel shared-structure dictionary learning approach combined with rule-based reasoning for continuous daily activity tracking and abnormal activities detection. WITS also exploits an Internet of Things middleware for the scalable and seamless management and learning of the information produced by ambient sensors. We further develop a user-friendly interface, which runs on both iOS and Andriod, as well as in Chrome, for the efficient customization of WITS monitoring services without programming efforts. This paper presents the architectural design of WITS, the core algorithms, along with our solutions to the technical challenges in the system implementation.

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Notes

  1. http://www.un.org/en/development/desa/population/publications/pdf/ageing/WorldPopulationAgeing2013.pdf.

  2. http://www.alientechnology.com/.

  3. https://www.arduino.cc/.

  4. https://www.raspberrypi.org/.

  5. http://www.phidgets.com/.

References

  1. Aharon M, Elad M, Bruckstein A (2006) SVD: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans Signal process 54(11):4311–4322

    Article  MATH  Google Scholar 

  2. Bhattacharya S, Nurmi P, Hammerla N, Plötz T (2014) Using unlabeled data in a sparse-coding framework for human activity recognition. Pervasive Mob Comput 15:242–262

    Article  Google Scholar 

  3. Bulling A, Blanke U, Schiele B (2014) A tutorial on human activity recognition using body-worn inertial sensors. ACM Comput Surv (CSUR) 46(3):33

    Article  Google Scholar 

  4. Chen J, Zhou J, Ye J (2011) Integrating low-rank and group-sparse structures for robust multi-task learning. In: Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, pp 42–50

  5. Chen L, Hoey J, Nugent CD, Cook DJ, Yu Z (2012) Sensor-based activity recognition. IEEE Trans Syst Man Cybern Part C Appl Rev 42(6):790–808

    Article  Google Scholar 

  6. Cook DJ, Schmitter-Edgecombe M, Dawadi P (2015) Analyzing activity behavior and movement in a naturalistic environment using smart home techniques. IEEE J Biomed Health Inform 19(6):1882–1892

    Article  Google Scholar 

  7. Dawadi PN, Cook DJ, Schmitter-Edgecombe M (2013) Automated cognitive health assessment using smart home monitoring of complex tasks. IEEE Trans Syst Man Cybern Syst 43(6):1302–1313

    Article  Google Scholar 

  8. Edwards WK, Grinter RE (2001) At home with ubiquitous computing: seven challenges. In: Ubicomp 2001: ubiquitous computing. Springer, pp 256–272

  9. Evgeniou A, Pontil M (2007) Multi-task feature learning. Adv Neural Inf Process Syst 19:41

    Google Scholar 

  10. Guha T, Ward RK (2012) Learning sparse representations for human action recognition. IEEE Trans Pattern Anal Mach Intell 34(8):1576–1588

    Article  Google Scholar 

  11. Hodrick RJ, Prescott EC (1997) Postwar US business cycles: an empirical investigation. J Money Credit Bank 1:1–16

    Article  Google Scholar 

  12. Intille S et al (2006) Using a live-in laboratory for ubiquitous computing research. In: Proceedings of international conference on pervasive computing (PERVASIVE)

  13. Kidd CD, Orr R, Abowd GD, Atkeson CG, Essa IA, MacIntyre B, Mynatt E, Starner TE, Newstetter W (1999) The aware home: a living laboratory for ubiquitous computing research. In: Streitz NA, Siegel J, Hartkopf V, Konomi S (eds) Cooperative buildings. Integrating information, organizations, and architecture. CoBuild 1999. Lecture Notes in Computer Science, vol 1670. Springer, Berlin, pp 191–198

  14. Lee H, Battle A, Raina R, Ng AY (2007) Efficient sparse coding algorithms. In: Advances in neural information processing systems. MIT Press, Cambridge, pp 801–808

  15. Mairal J, Bach F, Ponce J, Sapiro G (2009) Online dictionary learning for sparse coding. In: Proceedings of the 26th annual international conference on machine learning. ACM, pp 689–696

  16. Mennicken S, Vermeulen J, Huang EM (2014) From today’s augmented houses to tomorrow’s smart homes: new directions for home automation research. In: Proceedings of the 2014 ACM international joint conference on pervasive and ubiquitous computing. ACM, pp 105–115

  17. Minor B, Doppa JR, Cook DJ (2015) Data-driven activity prediction: algorithms, evaluation methodology, and applications. In: KDD, pp 805–814

  18. Mozer M (2004) Lessons from an adaptive house. Ph.D. thesis, University of Colorado

  19. Pintus A, Carboni D, Piras A (2012) Paraimpu: a platform for a social web of things. In: WWW, pp 401–404

  20. Rashidi P, Cook DJ (2011) Activity knowledge transfer in smart environments. Pervasive Mob Comput 7(3):331–343

    Article  Google Scholar 

  21. Sohn T, Dey A (2003) iCAP: an informal tool for interactive prototyping of context-aware applications. In: CHI, pp 974–975

  22. Taylor K et al (2013) Farming the web of things. IEEE Intell Syst 28(6):12–19

    Article  Google Scholar 

  23. Tung J, Snyder H, Hoey J, Mihailidis A, Carrillo M, Favela J (2013) Everyday patient-care technologies for Alzheimer’s disease. IEEE Pervasive Comput 12(4):80–83

    Article  Google Scholar 

  24. Ur B, McManus E, Pak Yong Ho M, Littman ML (2014) Practical trigger-action programming in the smart home. In: CHI, pp 803–812

  25. Wang H, Nie F, Huang H (2013) Multi-view clustering and feature learning via structured sparsity. In: ICML, no 3, pp 352–360

  26. Welbourne E, Battle L, Cole G, Gould K, Rector K, Raymer S, Balazinska M, Borriello G (2009) Building the internet of things using RFID: the RFID ecosystem experience. IEEE Internet Comput 13(3):48–55

    Article  Google Scholar 

  27. Yan Y, Ricci E, Subramanian R, Liu G, Sebe N (2014) Multitask linear discriminant analysis for view invariant action recognition. IEEE Trans Image Process 23(12):5599–5611

    Article  MathSciNet  MATH  Google Scholar 

  28. Yao L, Benatallah B, Wang X, Tran NK, Lu Q. Context as a service: realizing internet of things-aware processes for the independent living of the elderly. In: international conference on service-oriented computing ICSOC

  29. Yao L, Nie F, Sheng QZ, Gu T, Li X, Wang S (2016) Learning from less for better: semi-supervised activity recognition via shared structure discovery. In: Proceedings of the 2016 ACM international joint conference on pervasive and ubiquitous computing (UbiComp). ACM, pp 13–24

  30. Yao L, Sheng QZ, Li X, Gu T, Tan M, Wang X, Wang S, Ruan W (2018) Compressive representation for device-free activity recognition with passive RFID signal strength. IEEE Trans Mob Comput 17(2):293–306

    Article  Google Scholar 

  31. Yao L, Sheng QZ, Ngu AH, Li X, Benattalah B (2017) Unveiling correlations via mining human-thing interactions in the web of things. ACM Trans Intell Syst Technol (TIST) 8(5):62

    Google Scholar 

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Correspondence to Lina Yao.

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Yao, L., Sheng, Q.Z., Benatallah, B. et al. WITS: an IoT-endowed computational framework for activity recognition in personalized smart homes. Computing 100, 369–385 (2018). https://doi.org/10.1007/s00607-018-0603-z

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  • DOI: https://doi.org/10.1007/s00607-018-0603-z

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