Abstract
This paper investigates the development of a system for monitoring of dementia suffers living in their own homes. The system uses unobtrusive pervasive sensor and actuator devices that can be deployed within a patient’s home grouped and accessed via standardized platforms. For each sensor group our system uses unsupervised neural networks to identify the patient’s habitual behaviours based on their activities in the environment. Rule-based summarisation is used to provide descriptive rules representing the intra and inter activity variations within the discovered behaviours. We propose a model comparison mechanism to facilitate tracking of behaviour changes, which could be due to the effects of cognitive decline. We demonstrate using user data acquired from a real pervasive computing environment, how our system is able to identify the user’s prominent behaviours enabling assessment and future tracking.
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References
Society, About Dementia (June 30, 2011), http://alzheimers.org.uk/site/scripts/documents.php?categoryID=200120
Agrawal, R., Srikant, R.: Fast Algorithms for Mining Association Rules. In: Proceedings of the 20th International Conference on Very Large Databases, Santiago, pp. 487–499 (September 1994)
Ducatel, K., Bogdanowicz, M., Scapolo, F., Leijten, J., Burgelman, J.: Ambient intelligence: From vision to reality. In: Riva, G., Vatalaro, F., Davide, F., Alcaniz, M. (eds.) Ambient Intelligence: The Evolution of Technology Communication and Cognation Towards the Future of Human-Computer Interaction. Emerging Communication: Studies in New Technologies and Practices in Communication, vol. 6. IOS Press (2003)
Chiarugi, F., Zacharioudakis, G., Tsiknakis, M., Thestrup, J., Hansen, K.M., Antolin, P., Melgosa, J.C., Rosengren, P., Meadows, J.: Ambient Intelligence support for tomorrow’s Health Care: Scenario-based requirements and architectural specifications of the EU-Domain platform. In: Proceedings of the International Special Topic Conference on Informational Technology in BioMedicine, Ioannina, Greece, October 26-28 (2006)
OSGi Alliance (2011), http://www.osgi.org
Lee, S.W., Palmer-Brown, D., Tepper, J.A., Roadknight, C.M.: Snap-drift: real-time, performance-guided learning. In: International Joint Conference on Neural Networks, Portland, OR, USA, July 20-24, pp. 1412–1416. IEEE, Piscataway (2003)
Lee, S.W., Palmer-Brown, D., Roadknight, C.M.: Performance guided Neural Network for Rapidly Self Organising Active Network Management. Neurocomputing 61, 5–20 (2004)
Ishibuchi, H., Yamamoto, T.: Rule Weight Specification in Fuzzy Rule-Based Classification Systems. IEEE Transactions on Fuzzy Systems 13(4), 428–435 (2005)
Wu, D., Mendel, J.M., Joo, J.: Linguistic Summarization Using If-Then Rules. In: Proceedings of the IEEE International Conference on Fuzzy Systems, Barcelona, Spain, pp. 1–8 (July 2010)
Woolham, J., Gibson, G., Clark, P.: Assistive Technology, Telecare, and Dementia: Some Implications of Current Policies and Guidance. Research Policy and Planning 24(3), 149–164 (2007)
Conde, D., Ortigosa, J.M., Javier, F., Salinas, J.R.: Open OSGi Middleware to Integrate Wireless Sensor Devices into Ambient Assisted Living Environments. In: Proceedings of AALIANCE Conference, Malaga, Spain, March 11-12 (2010)
Xu, R., Wunsch, D.: Survey of Clustering Algorithms. IEEE Transaction on Neural Networks 16(3), 645–678 (2005)
Kohonen, T.: Self-Organisation and Asssociative Memory, 3rd edn. Springer, Heilderberg (1989)
Carpenter, G.A., Grossberg, S.: Adaptive Resonance Theory. The Handbook of Brain Theory and Neural Networks, 2nd edn., pp. 87–90. MIT Press, Cambridge (2003)
Arnrich, B., Mayora, O., Bardram, J., Troster, G.: Pervasive Healthcare: Paving the Way for a Pervasive, User-Centered and Preventive Healthcare Model. Methods of Information in Medicine 49(1), 67–73 (2010)
Peters, C., Wachsmuth, S., Hoey, J.: Learning to Recognise Behaviours of Persons with Dementia using Multiple Cues in an HMM-Based Approach. In: Proceedings of the 2nd International Conference on Pervasive Technologies Related to Assistive Environments, Corfu, Greece, June 23-25 (2009)
Mihailidis, A., Carmichael, B., Boger, J.: The Use of Computer Vision in an Intelligent Environment to Support Aging-in-Place, Safety, and Independence in the Home. IEEE Transactions on Information Technology in Biomedicine 8(3), 238–247 (2004)
Mynatt, E.D., Melenhorst, A.S., Fisk, A.D., Rogers, W.A.: Aware Technologies for Aging in Place: Understanding User Needs and Attitudes. Pervasive Computing 3(2), 36–41 (2004)
Hayes, T.L., Hunt, J.M., Adami, A., Kaye, J.A.: An Electronic Pillbox for Continuous Monitoring of Medication Adherence. In: Proceedings of the 28th IEEE EMBS Annual International Conference, New York City, USA (2006)
Matic, A., Mehta, P., Rehg, J.M., Osmani, V., Mayora, O.: Monitoring Dressing Activity Failures through RFID and Video. Methods of Information in Medicine 47(3), 229–234 (2008)
Biswas, J., et al.: Agitation Monitoring of Persons with Dementia based on Acoustic Sensors, Pressure Sensors and Ultrasound Sensors: A Feasibility Study. In: Proceedings of The International Conference on Aging, Disability and Independence, St. Petersburg, Florida, February 1-5, pp. 3–15. IOS Press, Amsterdam (2006)
Bonroy, B., et al.: Image Acquisition System to Monitor Discomfort in Demented Elderly Patients. In: Proceedings of the 18th ProRISC Annual Workshop on Circuits, Systems and Signal Processing, Veldhoven, The Netherlands, November 29-30 (2008)
Neergaard, L.: Can Motion Sensors Predict Dementia?. The Associated Press (June 19, 2007)
Mendel, J.: Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions. Prentice Hall PTR (2001)
Palmer-Brown, D., Lee, S.W., Draganova, C., Kang, M.: Modal learning neural networks. WSEAS Transactions on Computers 8(2), 222–236 (2009)
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Doctor, F., Jayne, C., Iqbal, R. (2012). Ambient Intelligent Monitoring of Dementia Suffers Using Unsupervised Neural Networks and Weighted Rule Based Summarisation. In: Jayne, C., Yue, S., Iliadis, L. (eds) Engineering Applications of Neural Networks. EANN 2012. Communications in Computer and Information Science, vol 311. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32909-8_37
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DOI: https://doi.org/10.1007/978-3-642-32909-8_37
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32908-1
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