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Hierarchical activity recognition for dementia care using Markov Logic Network

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Abstract

Statistics reveal that globally, the aging population in different stages of dementia are struggling to cope with daily activities and are progressively becoming dependent on care takers thereby making dementia care a challenging social problem. Healthcare systems in smart environments that aim to address this growing social need require the support of technology to recognize and respond in an ubiquitous manner. To incorporate an efficient activity recognition and abnormality detection system in smart environments, the routine activities of the occupant are modeled and any deviation from the activity model is recognized as abnormality. Recognition systems are generally designed using machine learning strategies and in this paper a novel hybrid, data and knowledge-driven approach is introduced. Markov Logic Network (MLN) used in our design is a suitable approach for activity recognition as it integrates common sense knowledge with a probabilistic model that augments the recognition ability of the system. The proposed activity recognition system for dementia care uses a hierarchical approach to detect abnormality in occupant behavior using MLN. The abnormality in the context of dementia care is identified in terms of factors associated with the activity such as objects, location, time and duration. The task of recognition is done in a hierarchical manner based on the priority of the factor that is associated with each layer. The motivation for designing a hierarchical approach was to enable each layer to commence its computation after inferring from the lower layers that the ongoing activity was normal with regard to the associated factors. This hierarchical feature enables quick decisions, as factors that require immediate attention are processed first at the lowest layer. Experimental results indicate that the hierarchical approach has higher accuracy in recognition and efficient response time when compared to the existing approaches.

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References

  1. Wimo A, Prince M (2010) World alzheimer report 2010: the global economic impact on dementia. Alzheimer Disease international, London

  2. Amoretti M, Copelli S, Wientapper F, Furfari F, Lenzi S, Chessa S (2013) Sensor data fusion for activity monitoring in the personal ambient assisted living project. J Ambient Intell Humaniz Comput 4(1):67–84

    Article  Google Scholar 

  3. Graham N, Warner J (2009) Understanding alzheimers disease and other dementias. British Medical Association, Family Doctor Books, Poole, Dorset, UK

  4. Augusto JC, Nakashima H, Aghajan H (2010) Ambient intelligence and smart environments: a state of the art. In: Handbook of ambient intelligence and smart environments, pp 3–31. doi:10.1007/978-0-387-93808-0_1

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

    Article  Google Scholar 

  6. Cook DJ, Augusto JC, Jakkula VR (2009) Ambient intelligence: technologies, applications, and opportunities. Pervasive Mob Comput 5(4):277–298

    Article  Google Scholar 

  7. Cook DJ, Das SK (2007) How smart are our environments? An updated look at the state of the art. Pervasive Mob Comput 3(2):53–73

    Article  Google Scholar 

  8. Alam MR, Reaz MBI, Ali MAM (2012) A review of smart homes—past, present, and future. IEEE Trans Syst Man Cybern C 42(6):1190–1203

    Article  Google Scholar 

  9. Cook DJ (2012) Learning setting-generalized activity models for smart spaces. IEEE Intell Syst 27(1):32–38

    Article  Google Scholar 

  10. Atallah L, Yang G-Z (2009) The use of pervasive sensing for behaviour profiling—a survey. Pervasive Mob Comput 5(5):447–464

    Article  Google Scholar 

  11. Aztiria A, Izaguirre A, Augusto JC (2010) Learning patterns in ambient intelligence environments: a survey. Artif Intell Rev 34(1):35–51

    Article  Google Scholar 

  12. Rashidi P, Cook DJ (2009) Keeping the resident in the loop: adapting the smart home to the user. IEEE Trans Syst Man Cybern A 39(5):949–959

    Article  Google Scholar 

  13. Rashidi P, Cook DJ, Holder LB, Schmitter-Edgecombe M (2011) Discovering activities to recognize and track in a smart environment. IEEE Trans Knowl Data Eng 23(4):527–539

    Article  Google Scholar 

  14. Chen L, Nugent CD, Wang H (2012) A knowledge-driven approach to activity recognition in smart homes. IEEE Trans Knowl Data Eng 24(6):961–974

    Article  Google Scholar 

  15. Riboni D, Bettini C (2011) Cosar: hybrid reasoning for context-aware activity recognition. Pers Ubiquitous Comput 15(3):271–289

    Article  Google Scholar 

  16. Okeyo G, Chen L, Wang H, Sterritt R (2012) A hybrid ontological and temporal approach for composite activity modelling. In TrustCom, pp 1763–1770

  17. Natarajan S, Bui HH,Tadepalli P, Kersting K, Wong W-K (2008) Logical hierarchical hidden markov models for modeling user activities. In: Zelezn F, Lavrac N (eds) ILP, volume 5194 of Lecture Notes in Computer Science. Springer, pp 192–209

  18. Chahuara P, Fleury A, Portet F, Vacher M (2012) Using markov logic network for on-line activity recognition from non-visual home automation sensors. In: Patern F, de Ruyter Boris E. R., Markopoulos P, Santoro C, van Loenen E, Luyten K (eds) Ambient intelligence, volume 7683 of Lecture Notes in Computer Science, Springer, Berlin, pp 177–192

  19. Richardson M, Domingos P (2006) Markov logic networks. Mach Learn 62(1–2):107–136

    Article  Google Scholar 

  20. Tran SD, Davis LS (2008) Event modeling and recognition using markov logic networks. In: Proceedings of the 10th European conference on computer vision: part II, ECCV ’08. Springer, Berlin, pp 610–623

  21. Helaoui R, Niepert M, Stuckenschmidt H (2010) A statistical–relational activity recognition framework for ambient assisted living systems. In: Augusto JC, Corchado JM, Novais P, Analide C (eds) ISAm I, volume 72 of Advances in Soft Computing. Springer, Berlin, pp 247–254

  22. Helaoui R, Niepert M, Stuckenschmidt H (2011) Recognizing interleaved and concurrent activities: a statistical–relational approach. In PerCom, IEEE, pp 1–9

  23. Tran AC, Marsland S, Dietrich J, Guesgen HW, Lyons P (2010) Use cases for abnormal behaviour detection in smart homes. In: Lee Y, Zenn Bien Z, Mokhtari M, Jeong Tai K, Mignon P, Jongbae K, Heyoung L, Ismail K (eds) ICOST, Lecture notes in computer science, vol 6159. Springer, pp 144–151. doi:10.1007/978-3-642-13778-5_18

  24. Gayathri KS, Elias S, Shivashankar S (2014) An ontology and pattern clustering approach for activity recognition in smart environments. Volume 258 of Advances in Intelligent Systems and Computing, Springer, Berlin, pp 833–843

  25. http://guangchun.files.wordpress.com/2012/03/mlnprob1.pdf

  26. http://www.mpi-inf.mpg.de/departments/d5/teaching/ss12/sum/slides/06-markov-logic-handout.pdf

  27. Ordóñez FC, de Toledo P, Sanchis A (2013) Activity recognition using hybrid generative/discriminative models on home environments using binary sensors. Sensors 13(5):5460–5477. doi:10.3390/s130505460

  28. Sow DM, Turaga DS, Schmidt M (2013) Mining of sensor data in healthcare: a survey. In: Aggarwal CC (ed) Managing and mining sensor data. Springer, Berlin, pp 459–504

  29. Kim E, Helal S, Cook D (2010) Human activity recognition and pattern discovery. IEEE Pervasive Comput 9(1):48–53

    Article  Google Scholar 

  30. Singla G, Cook DJ, Schmitter-Edgecombe M (2010) Recognizing independent and joint activities among multiple residents in smart environments. J Ambient Intell Humaniz Comput 1(1):57–63

    Article  Google Scholar 

  31. http://alchemy.cs.washington.edu/

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Authors and Affiliations

  1. Sri Venkateswara College of Engineering, Pennalur, Sriperumbudur Taluk, 602117, Tamilnadu, India

    K. S. Gayathri & Susan Elias

  2. Indian Institute of Technology Madras, Chennai, 600 036, Tamilnadu, India

    Balaraman Ravindran

Authors
  1. K. S. Gayathri
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  2. Susan Elias
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  3. Balaraman Ravindran
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Correspondence to K. S. Gayathri.

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Gayathri, K.S., Elias, S. & Ravindran, B. Hierarchical activity recognition for dementia care using Markov Logic Network. Pers Ubiquit Comput 19, 271–285 (2015). https://doi.org/10.1007/s00779-014-0827-7

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  • DOI: https://doi.org/10.1007/s00779-014-0827-7

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