生活樣式能表現出人的生活方式。每個人都有不同的生活方式，若能知道一個人的生活方式，我們可以將它應用於許多領域，例如推薦系統、情境感知、健康照護、老人異常偵測等。在本研究中，我們提出生活樣式探勘並應用於老人異常偵測。過去的研究中，已有生活樣式探勘架構被提出，但由於缺少足夠的資訊，傳統基於單種類的感測器資料的生活樣式無法完整的表示一個人的生活樣式，且用這樣的生活樣式來做異常偵測會較不準確。有鑒於此，在本研究中，我們基於穿戴式裝置中之多種類感測器(例如生理感測器、GPS感測器等)來探勘生活樣式，讓我們可以更探勘出完整的生活樣式。在將生活樣式應用於老人異常偵測時，我們亦將老人的狀態(例如健康狀況) 納入考量，並在偵測到異常時進一步判斷是否為緊急事件。在實驗部份，我們設計了一個資料模擬器來產生老人的生活模擬資料，並經由廣泛的實驗評估來驗證我們所提出方法之執行效益。

Life patterns can represent an individual’s life style and they can help people understand what they do in a certain time as well as the regular habits. The discovery of life patterns has a manifold of application scenarios, which can be embedded into location-based recommender systems, precise advertising, computer-aided scheduling, and care/alert systems. In this thesis, we propose an approach for life style mining with applications on elderly anomaly detection. Although there are existing works for discovering life styles, they are based on single sensor environment traditionally. Consequently, it cannot completely represent an individual’s lifestyle due to the lack of sufficient information and related applications like anomaly detection cannot reach high accuracy. To deal with above-mentioned problems, we mine an individual’s life pattern from wearable-devices-based environment with multiple kinds of sensors. When we apply the life patterns to elderly anomaly detection, multiple-sensors-based elderly’s conditions, such as physical condition and locations, are taken into considerations at the same time for anomaly detection. Once an anomaly is detected, it is further evaluated to distinguish whether the anomaly is urgent. For experimental evaluations, we design a data simulator to generate sensors data of elderly’s daily life, based on which the effectiveness of our proposed framework is verified.

[1] A.K. Bourke, P. W.J. van de Ven, A. E. Chaya, G.M. OLaighin and J. Nelson. “Testing of a long-term fall detection system incorporated into a custom vest for the elderly”. Engineering in Medicine and Biology Society, pages: 2844-2847, Vancouver, BC, Aug., 2008.
[2] A. Bourouis, M. Feham and A. Bouchachia, “UBIQUITOUS MOBILE HEALTH MONITORING SYSTEM FOR ELDERLY (UMHMSE)”, Internation Journal of Computer Science & Information Technology(IJCSIT), 2011.
[3] B. Gardner, P. Lally and J. Wardle. “Making health habitual: the psychology of ‘habit-formation’ and general practice”. The British journal of general practice: the journal of the Royal College of General Practitioners 62:605, pages: 664-666, Dec., 2012.
[4] Y.-J. Hong, L.-J. Kim and H.-G. Kim, “Activity Recognition Using Wearable Sensors for Elder Care”, International Conference on Future Generation Communication and Networking, pages: 302-305, Hainan Island, Dec., 2008.
[5] Z. Hajihashemi and M. Popescu, “Detection of Abnormal Sensor Patterns in Eldercare”, In Proceedings of the 4th IEEE International Conference on E-Health and Bioengineering, pages 1-4, Iasi, Romania, November, 2013.
[6] H.-H. Hsu and C.-C. Chen, “RFID-Based Human Behavior Modeling and Anomaly Detection for Elderly Care”, Mobile Information System, 6(4), pages:341-354, 2010.
[7] Pei, J., Han, J., Mortazavi-Asl, B., Pinto, H., Chen, Q., Dayal, U., & Hsu, M. C. (2001, April). Prefixspan: Mining sequential patterns efficiently by prefix-projected pattern growth. In icccn (p. 0215). IEEE.
[8] V. Jakkula and D.J. Cook, “Detecting Anomalous Sensor Events in Smart Home Data for Enhancing the Living Experience”, In Artificial Intelligence and Smarter Living, volume WS-11-07 of AAAI Workshops, pages 33-37, 2011.
[9] M. Katsukura, M. Nakata, and N. Kushiro, “Life pattern sensor with non-intrusive appliance monitoring”, In: Consumer Electronics, 2009. ICCE'09. Digest of Technical Papers International Conference on. IEEE, pages 1-2, Las Vegas, NV, Jan. 2009.
[10] A.M. Khan, Y.-K. Lee, S.Y. Lee and T.-S. Kim, “Accelerometer’s position independent physical activity recognition system for long-term activity monitoring in the elderly”, Medical & Biological Engineering & Computing, Volume 48, Issue 12, pages: 1271-1279, November, 2010.
[11] ZA. Khan and W. Sohn, “A Model for Abnormal Activity Recognition and Alert Generation System for Elderly Care by Hidden Conditional Random Fields Using R-Transform and Generalized Discriminant Analysis Features”, Telemedicine & e-Health, Oct, 2012.
[12] M. Lilja and L. Borell, “Elderly people’s daily activities and need for mobility support”, Scandinavian Journal of Caring Sciences, pages 73-80, 1997.
[13] M. Novak, M. Binas, and F. Jakab, “Unobtrusive Anomaly Detection in Presence of Elderly in a Smart-home Environment”, In Proceedings of the International Conference on ELEKTRO, pages 341-344, Rajeck Tepplice, Slovakia, May, 2012.
[14] T. Mori, T. Ishino, H. Noguchi, T. Sato, Y. Miura, G. Nakagami, M. Oe, and H. Sanada, “Life Pattern Estimation of the Elderly Based on Accumulated Activity Data and its Application to Anomaly Detection”, Journal of Robotics and Mechatronics, Vol.24, No.5, 2012.
[15] T. Mori, R. Urushibata, M. Shimosaka, H. Noguchi, and T. Sato, “Anomaly detection algorithm based on life pattern extraction from accumulated pyroelectric sensor data”, Intelligent Robots and Systems, pages: 22-26, Sept, 2008.
[16] K. Ouchi and M. Doi, “Smartphone-based Monitoring System for Activities of Daily Living for Elderly People and Their Relatives Etc.”, Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication, pages: 103-106, New York, USA, 2013.
[17] L. R. Pina, E. Ramirez, and W. G. Griswold, “Fitbit+: A behavior-based intervention system to reduce sedentary behavior” In Pervasive Computing Technologies for Healthcare (PervasiveHealth), 2012 6th International Conference on (pp. 175-178). IEEE, pages:175-178, May, 2012.
[18] U. Sonn, “Assessment of activities of daily living in the elderly. A study of a population of 76-year-olds in Gothenburg”, Scandinavian journal of rehabilitation medicine, pages:193-202, 1990.
[19] U Sonn, “Longitudinal studies of dependence in daily life activities among elderly person”, Scand J Rehabil Med Suppl, 1996.
[20] V. SRINIVASAN, S. Moghaddam, “Mobileminer: Mining your frequent patterns on your phone” In: Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM, p. 389-400, 2014.
[21] T.-H. Tan, K.-H. Chen, F.-R. Jean, Y.-F. Chen, F.-J. Shih and C. F. Ho, “Indoor activity monitoring system for elderly using RFID and Fitbit Flex wristband”, International Conference on Biomedical and Health Informatics, pages: 41-44, Valencia, June, 2014.
[22] H.-H. Tsai and S.-Y. Tzeng. “A Comparison of Lifestyle and Behavior Pattern for the Elderly at Senior Apartment and the Elderly Living Alone at Home－ By the Cases in Kaohsiung Area”. National Yunlin University of Science and Technology, Department of Architecture and Interior Design, Master Thesis. p.123
[23] A. Vahdatpour, and M. Sarrafzadeh, “Unsupervised Discovery of Abnormal Activity Occurrences in Multi-dimensional Time Series”, with Applications in Wearable Systems. In: SDM, pages:641-652, 2010.
[24] K. Wang, and K. M. Wang. "An Exploratory Study on Leisure Activities Demand of Elderly in Taiwan." The 10th Academic and Business Research Institute Conference Las Vegas. 2011.
[25] Y. Ye, Y. Zheng, Y. Chen, J. Feng, and X. Xie, “Mining Individual Life Pattern Based on Location History”, In Proceedings of the International Conference on Mobile Data Management, pages 1-10, Taipei, Taiwan, May, 2009.
[26] T. Zhao, H. Ni, X. Zhou, L. Qiang, D. Zhang and Z. Yu. Detecting abnormal patterns of daily activities for the elderly living alone. In Health Information Science; Zhang, Y., Yao, G., He, J., Wang, L., Smalheiser, N.R., Yin, X., Eds.; Springer International Publishing: pp. 95–108, New York, NY, USA, 2014.
[27] Keogh, Eamonn, and Chotirat Ann Ratanamahatana. "Exact indexing of dynamic time warping." Knowledge and information systems 7.3 (2005): 358-386.
[28] Fitbit – Available at (https://www.fitbit.com/)
[29] Jawbone UP – Available at (https://jawbone.com/)
[30] Nike FuelBand – Available at (https://www.nike.com/us/en_us/p/activity)
[31] World Health Organization – Ageing. Available at (http://www.who.int/topics/ageing/en/)