Abstract
Since the dramatic demographic change makes it inevitable that rapid aging of the population is an unprecedented phenomenon in Taiwan. A growing social problem is supporting older adults who want to live independently in their own homes. It needs a health assistance system to make them independent living up to a higher age. Recently, technological advancements have spurred various ideas and innovations to assist the elders living independently. In this paper, we proposed a homecare sensory system that uses RFID-based sensor networks to collect elder’s daily activities and conducts the data into Hidden Markov model (HMM) and Support Vector Machines (SVMs) to estimate whether the elder’s behavior is abnormal or not. Through detecting and distinguishing the abnormal behaviors of elder’s daily activities, the system provides assistance on elder’s independent living and improvement of aged quality of life.
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References
World Health Organization, http://www.who.int/en/
Ministry of the Interior, http://www.moi.gov.tw/stat/
Bouma, H.: Gerontechnology: Making technology relevant for the elderly. In: Bouma, H., Graafmans, J.A.M. (eds.) Gerontechnology, pp. 1–5. IOS Press, Amsterdam (1992)
Lawton, M.P., Brody, E.M.: Assessment of older people: self-maintaining and instrumental activities of daily living. Gerontologist 9, 179–186 (1969)
Rogers, W.A., Meyer, B., Walker, N., Fisk, A.D.: Functional limitations to daily living tasks in the aged: a focus groups analysis. Human Factors 40, 111–125 (1998)
Tapia, E.M., Intille, S.S., Larson, K.: Activity recognition in the home using simple and ubiquitous sensors. In: Ferscha, A., Mattern, F. (eds.) PERVASIVE 2004. LNCS, vol. 3001, pp. 158–175. Springer, Heidelberg (2004)
Wilson, D., Atkeson, C.: Simultaneous tracking and activity recognition (star) using many anonymous binary sensors. In: Gellersen, H.-W., Want, R., Schmidt, A. (eds.) PERVASIVE 2005. LNCS, vol. 3468, pp. 62–79. Springer, Heidelberg (2005)
Living independently - quietcare system, http://www.livingindependently.com
Duong, T.V., Bui, H.H., Phung, D.Q., Venkatesh, S.: Activity recognition and abnormality detection with the switching hidden semi-markov model. In: CVPR 2005: Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), Washington, DC, USA, vol. 1, pp. 838–845. IEEE Computer Society, Los Alamitos (2005)
Lester, J., Choudhury, T., Kern, N., Borriello, G., Hannaford, B.: A hybrid discriminative/generative approach for modeling human activities. In: IJCAI, pp. 766–772 (2005)
Patterson, D.J., Fox, D., Kautz, H.A., Philipose, M.: Fine-grained activity recognition by aggregating abstract object usage. In: ISWC, pp. 44–51. IEEE Computer Society, Los Alamitos (2005)
Schmidt, A.: Ubiquitous Computing – Computing in Context. PhD thesis, Lancaster University (2002)
Liao, L., Fox, D., Kautz, H.: Learning and Inferring Transportation Routines. In: Proc. 19th Nat’l Conf. Artificial Intelligence (AAAI 2004), July 2004, pp. 348–353 (2004)
Patterson, D.J., Liao, L., Fox, L., Kautz, H.: Inferring High-Level Behavior from Low- Level Sensors. In: Dey, A.K., Schmidt, A., McCarthy, J.F. (eds.) UbiComp 2003. LNCS, vol. 2864, pp. 73–89. Springer, Heidelberg (2003)
Yin, J., Chai, X., Yang, Q.: High-Level Goal Recognition in a Wireless LAN. In: Proc. 19th Nat’l Conf. in Artificial Intelligence (AAAI 2004), July 2004, pp. 578–584 (2004)
Rabiner, L.R.: A tutorial on hidden markov models and selected applications in speech recognition. Proceedings of the IEEE 77(2), 257–286 (1989)
Sánchez, D., Tentori, M., Favela, J.: Activity Recognition for the Smart Hospital. IEEE Intelligent Systems 23(2), 50–57 (2008)
Favela, J., et al.: Activity Recognition for Context-Aware Hospital Applications: Issues and Opportunities for the Deployment of Pervasive Networks. Mobile Networks and Applications 12(2-3), 155–171 (2007)
Bradley, A.P.: The Use of the Area under the ROC Curve in the Evaluation of Machine Learing Algorithms. Pattern Recognition 30, 1145–1159 (1997)
Breunig, M.M., Kriegel, H.P., Ng, R., Sander, J.: Identifying Density-Based Local Outliers. In: Proc. ACM SIGMOD Int’l Conf. Management of Data (SIGMOD 2000), May 2000, pp. 93–104 (2000)
Chan, P., Stolfo, S.: Toward Scalable Learning with Non Uniform Class and Cost Distributions. In: Proc. Fourth Int’l Conf. Knowledge Discovery and Data Mining (KDD 1998), August 1998, pp. 164–168 (1998)
Domingos, P.: Metacost: A General Method for Making Classifiers Cost-Sensitive. In: Proc. Fifth Int’l Conf. Knowledge Discovery and Data Mining (KDD 1999), August 1999, pp. 155–164 (1999)
Xiang, T., Gong, S.: Video Behaviour Profiling and Abnormality Detection without Manual Labeling. In: Proc. IEEE Int’l Conf. Computer Vision (ICCV 2005), October 2005, pp. 1238–1245 (2005)
Duong, T., Bui, H., Phung, D., Venkatesh, S.: Activity Recognition and Abnormality Detection with the Switching Hidden Semi-Markov Model. In: Proc. IEEE Int’l Conf. Computer Vision and Pattern Recognition (CVPR 2005), June 2005, pp. 838–845 (2005)
Huang, K.-T.: An Intelligent RFID System for Improving Elderly Daily Life Independent in Indoor Environment. In: Helal, S., Mitra, S., Wong, J., Chang, C.K., Mokhtari, M. (eds.) ICOST 2008. LNCS, vol. 5120, pp. 1–8. Springer, Heidelberg (2008)
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Hung, YX., Chiang, CY., Hsu, S.J., Chan, CT. (2010). Abnormality Detection for Improving Elder’s Daily Life Independent. In: Lee, Y., et al. Aging Friendly Technology for Health and Independence. ICOST 2010. Lecture Notes in Computer Science, vol 6159. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13778-5_23
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DOI: https://doi.org/10.1007/978-3-642-13778-5_23
Publisher Name: Springer, Berlin, Heidelberg
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