Abstract
Internet of Things (IoT) is becoming one of hottest research topics. Elderly care is one of important applications in IoT, to grasp the situations around the elder people and then corresponding information can be sent to the care-givers to support the elder people. Abnormal activity detection is a particularly important task in the field, since the services should be immediately provided in such cases. Otherwise the elder people may be in danger. The existing approaches to this problem use some basic living patterns of the elder people, e.g. mobility per day, to detect abnormal activities. However, the detail abnormal activities in various specific situations cannot be detected, e.g., whether there is some abnormal activity when the elder people go to toilet, sleeps or eats something. To solve the above problem, in the paper, we propose a situation-aware abnormality detection system based on SVDD for the elder people. An experiment has been performed focusing on feasibility of the method and accuracy of the system to detect situations and abnormities from real sensors.
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References
Asia: Japan: Most Elderly Nation. The New York Times. (July 01, 2006) (retrieved January 07, 2008)
Mannini, A., Sabatini, A.M.: Machine learning methods for classifying human physical activity from on-body accelerometers. Journal of Sensors 10, 1154–1175 (2010)
Preece, S.J., Goulermas, J.Y., Kenney, L.P.J., Howard, D.: A Comparison of Feature Extraction Methods for the Classification of Dynamic Activities From Accelerometer Data. IEEE Trans. on Biomedical Engineering 56(3) (March 2009)
Han, C.W., Kang, S.J., Kim, N.S.: Implementation of HMM-Based Human Activity Recognition Using Single Trivial Accelerometer. IEICE Trans. on Fundamentals E93-A(7) (July 2010)
He, Z., Jin, L.: Activity Recognition From Acceleration Data Using AR Model Representation And SVM. In: Proc. of the Seventh International Conference on Machine Learning and Cybernetics, Kunming, July 12-15 (2008)
Park, K., Lin, Y., Metsis, V., Le, Z., Makedon, F.: Abnormal Human Behavior Detection in Assisted Living Environments. In: Proc. of PETRA 2010, Samos, Greece, June 23-25 (2010)
Burchfield, T.R., Venkatesan, S.: Accelerometer-Based Human Abnormal Movement Detection in Wireless Sensor Networks. In: Proc. of HealthNet 2007, San Juan, Puerto Rico, USA, June 11 (2007)
Yin, J., Yang, Q., Pan, J.: Sensor-Based Abnormal Human-Activity Detection. IEEE Trans. on Knowledge and Data Engineering 20(8) (August 2008)
Hu, D.H., Zhang, X., Yin, J., Zheng, V.W., Yang, Q.: Abnormal Activity Recognition Based on HDP-HMM Models. In: Proc. of IJCAI 2009, USA (2009)
Shin, J.H., Lee, B., Park, K.S.: Detection of Abnormal Living Patterns for Elderly Living Alone Using Support Vector Data Description. IEEE Trans. on Information Technology in Biomedicine 15(3) (May 2011)
Tax, D.M.J., Duin, R.P.W.: Support Vector Data Description. Mach. Learn. 54(1), 45–66 (2004)
Tax, D.M.J., Duin, R.P.W.: Support Vector Domain Description. Pattern Recognition, Letter 20(11-13), 1191–1199 (1999)
Ichizawa, T., Tosa, M., Kansen, M., Yishiyama, A., Cheng, Z.: The Attribute and Position based Ubiquitous Development Environment Using Antennas with an Automatically Switch, IPSJ SIG Technical Reports, No. 14, pp. 109–114 (2006) ISSN 0919-6072
Wang, J., Cheng, Z., Jing, L., Ota, K., Kansen, M.: A Two-stage Composition Method for Danger-aware Services based on Context Similarity. IEICE Transactions on Information and Systems E93-D(6), 1521–1539 (2010)
http://www.intellilink.co.jp/solutions/green/products/xechno-tap.html
Nanada, C., Fanale, J.E., Kronhom, P.: The Role of Medication Noncompliance and Adverse Drug Reactions in Hospitalizations of the elderly peoplely. Archives of Internal Medicine 150(4), 841–846 (1990)
Medications and the Older Adult, http://www.chryslerretirees.com/benefits2/hrdocs.chrysler.com/dashboard/dash/dash_images/medication.pdf
Jing, L., Zhou, Y., Cheng, Z., Wang, J.: A Recognition Method for One-Stroke Finger Gestures Using a MEMS 3D Accelerometer. IEICE Transactions on Information and Systems E94.D(5), 1062–1072 (2011)
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Wang, J., Cheng, Z., Zhang, M., Zhou, Y., Jing, L. (2012). Design of a Situation-Aware System for Abnormal Activity Detection of Elderly People. In: Huang, R., Ghorbani, A.A., Pasi, G., Yamaguchi, T., Yen, N.Y., Jin, B. (eds) Active Media Technology. AMT 2012. Lecture Notes in Computer Science, vol 7669. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35236-2_57
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DOI: https://doi.org/10.1007/978-3-642-35236-2_57
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