Abstract
In order to reduce the potential risks associated with physically and cognitively impaired ability of the elderly living alone, in this work, we develop an automated method that is able to detect abnormal patterns of the elderly’s entering and exiting behaviors collected from simple sensors equipped in home-based setting. With spatiotemporal data left by the elderly when they carrying out daily activities, a Markov Chains Model (MCM) based method is proposed to classify abnormal sequences via analyzing the probability distribution of the spatiotemporal activity data. The experimental evaluation conducted on a 128-day activity data of an elderly user shows a high detection ratio of 92.80% for individual activity and of 92.539% for the sequence consisting of a series of activities.
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References
Lymberopoulos, D., Bamis, A., Savvides, A.: Extracting spatiotemporal human activity patterns in assisted living using a home sensor network. Universal Access in the Information Society 10(2), 125–138 (2011)
Lin, Q., Zhang, D., Li, D., et al.: Extracting Intra-and Inter-activity Association Patterns from Daily Routines of Elders. Inclusive Society: Health and Wellbeing in the Community, and Care at Home, pp. 36–44. Springer, Heidelberg (2013)
Levenshtein, V.I.: Binary codes capable of correcting deletions, insertions and reversals. Soviet Physics Doklady 10, 707 (1966)
Krishnan, N.C., Panchanathan, S.: Analysis of low resolution accelerometer data for continuous human activity recognition. In: IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2008, pp. 3337–3340. IEEE (2008)
Győrbíró, N., Fábián, Á., Hományi, G.: An activity recognition system for mobile phones. Mobile Networks and Applications 14(1), 82–91 (2009)
Kwapisz, J.R., Weiss, G.M., Moore, S.A.: Activity recognition using cell phone accelerometers. ACM SIGKDD Explorations Newsletter 12(2), 74–82 (2011)
Maurer, U., Smailagic, A., Siewiorek, D.P., et al.: Activity recognition and monitoring using multiple sensors on different body positions. In: Maurer, U., Smailagic, A., Siewiorek, D.P., et al. (eds.) International Workshop on Wearable and Implantable Body Sensor Networks, BSN 2006, vol. 4, p. 116. IEEE (2006)
Liang, Y., Zhou, X., Yu, Z., et al.: Energy-Efficient Motion Related Activity Recognition on Mobile Devices for Pervasive Healthcare. Mobile Networks and Applications 1–15 (2013)
Rashidi, P., Cook, D.J.: Mining and monitoring patterns of daily routines for assisted living in real world settings. In: Proceedings of the 1st ACM International Health Informatics Symposium, pp. 336–345. ACM (2010)
Yu, Z., Yu, Z., Zhou, X., et al.: Tree-based mining for discovering patterns of human interaction in meetings. IEEE Transactions on Knowledge and Data Engineering 24(4), 759–768 (2012)
Ni, H., Abdulrazak, B., Zhang, D., et al.: Towards non-intrusive sleep pattern recognition in elder assistive environment. Journal of Ambient Intelligence and Humanized Computing 3(2), 167–175 (2012)
Aliakbarpour, H., Khoshhal, K., Quintas, J., Mekhnacha, K., Ros, J., Andersson, M., Dias, J.: HMM-Based Abnormal Behaviour Detection Using Heterogeneous Sensor Network. In: Camarinha-Matos, L.M. (ed.) Technological Innovation for Sustainability. IFIP AICT, vol. 349, pp. 277–285. Springer, Heidelberg (2011)
Duong, T.V., Bui, H.H., Phung, D.Q., et al.: Activity recognition and abnormality detection with the switching hidden semi-markov model. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 1, pp. 838–845. IEEE (2005)
Khan, Z.A., Sohn, W.: Feature extraction and dimensions reduction using R transform and Principal Component Analysis for abnormal human activity recognition. In: 2010 6th International Conference on Advanced Information Management and Service (IMS), pp. 253–258. IEEE (2010)
Krishnan, N., Cook, D.J., Wemlinger, Z.: Learning a Taxonomy of Predefined and Discovered Activity Patterns
Shin, J.H., Lee, B., Park, K.S.: Detection of abnormal living patterns for elderly living alone using support vector data description. IEEE Transactions on Information Technology in Biomedicine 15(3), 438–448 (2011)
Jung, H.Y., Park, S.H., Park, S.J.: Detection abnormal pattern in activities of daily living using sequence alignment method. In: 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2008, pp. 3320–3323. IEEE (2008)
Park, K., Lin, Y., Metsis, V., et al.: Abnormal human behavioral pattern detection in assisted living environments. In: Proceedings of the 3rd International Conference on PErvasive Technologies Related to Assistive Environments, vol. 9. ACM (2010)
Nazerfard, E., Rashidi, P., Cook, D.J.: Discovering temporal features and relations of activity patterns. In: 2010 IEEE International Conference on Data Mining Workshops (ICDMW), pp. 1069–1075. IEEE (2010)
Niu, Y., Zhang, C.: Comparation of String Similarity Algorithm. Computer and Digital Engineering 40(3), 14–17 (2012)
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Zhao, T., Ni, H., Zhou, X., Qiang, L., Zhang, D., Yu, Z. (2014). Detecting Abnormal Patterns of Daily Activities for the Elderly Living Alone. In: Zhang, Y., Yao, G., He, J., Wang, L., Smalheiser, N.R., Yin, X. (eds) Health Information Science. HIS 2014. Lecture Notes in Computer Science, vol 8423. Springer, Cham. https://doi.org/10.1007/978-3-319-06269-3_11
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DOI: https://doi.org/10.1007/978-3-319-06269-3_11
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