R. Agrawal and R. Srikant, Fast algorithms for mining association rules, Proc. of 1994 Int. Conf. Very Large Data Bases (VLDB94), pp.487-499, 1994.

J. Botia, A. Villa, P. , and J. , Ambient Assisted Living system for in-home monitoring of healthy independent elders, Expert Systems with Applications, vol.39, issue.9, pp.8136-8148, 2012.
DOI : 10.1016/j.eswa.2012.01.153

B. Cheng, Y. Tsai, G. Liao, and E. Byeon, HMM machine learning and inference for Activities of??Daily Living recognition, The Journal of Supercomputing, vol.13, issue.3, pp.29-42, 2010.
DOI : 10.1007/s11227-009-0335-0

S. Chernbumroong, S. Cang, A. Atkins, Y. , and H. , Elderly activities recognition and classification for applications in assisted living. Experts systems with Applications 40, pp.1662-1674, 2013.

B. Chikhaoui, S. Wang, and H. Pigot, A new algorithm based on sequential pattern mining for person identification in ubiquitous environments, KDD Workshop on Knowledge Discovery from Sensor Data, pp.19-28, 2010.

B. Chikhaoui, S. Wang, and H. Pigot, A Frequent Pattern Mining Approach for ADLs Recognition in Smart Environments, 2011 IEEE International Conference on Advanced Information Networking and Applications, pp.248-255, 2011.
DOI : 10.1109/AINA.2011.13

M. Danancher, J. Lesage, L. Litz, and G. Faraut, Indoor Location Tracking Based on a Discrete Event Model, Proc. of the IEEE International Conference on Systems, Man, and Cybernetics -SMC 2013, 2013.
DOI : 10.1007/978-3-642-30779-9_40

URL : https://hal.archives-ouvertes.fr/hal-00709173

T. Dimitrov, J. Pauli, and E. Naroska, Unsupervised Recognition of ADLs, Proc. of the 6th Hellenic conference on Artificial Intelligence: theories, models and applications, pp.71-80, 2010.
DOI : 10.1007/978-3-642-12842-4_11

J. Han, J. Pei, Y. , and Y. , Mining frequent patterns without candidate generation, Proc. of 2000 ACM-SIGMOD Int. Conf. Management of Data (SIGMOD00), pp.1-12, 2000.

S. Intille, E. Tapia, J. Rondoni, J. Beaudin, C. Kukla et al., Tools for Studying Behavior and Technology in Natural Settings, Proc. of UBICOMP 2003, pp.157-174, 2003.
DOI : 10.1007/978-3-540-39653-6_13

R. Kadouche, H. Pigot, B. Abdulrazak, and S. Giroux, Support Vector Machines for Inhabitant Identification in Smart Houses, pp.83-95, 2010.
DOI : 10.1007/978-3-642-16355-5_9

T. Kleinberger, M. Becker, E. Ras, A. Holzinger, and P. Muller, Ambient Intelligence in Assisted Living: Enable Elderly People to Handle Future Interfaces, Universal Access in Human-Computer Interaction, pp.103-112, 2007.
DOI : 10.1007/978-3-540-73281-5_11

M. Magnusson, Discovering hidden time patterns in behavior: T-patterns and their detection, Behavior Research Methods, Instruments, & Computers, vol.20, issue.1, pp.93-110, 2000.
DOI : 10.3758/BF03200792

H. Mannila, H. Toivonen, and A. I. Verkamo, Discovering frequent episodes in sequences, Proc. of KDD'95, pp.210-215, 1995.

J. Nehmer, M. Becker, A. Karshmer, and R. Lamm, Living assistance systems, Proceeding of the 28th international conference on Software engineering , ICSE '06, pp.43-50, 2006.
DOI : 10.1145/1134285.1134293

D. Patterson, D. Fox, H. Kautz, and M. Philipose, Fine-Grained Activity Recognition by Aggregating Abstract Object Usage, Ninth IEEE International Symposium on Wearable Computers (ISWC'05), pp.44-51, 2005.
DOI : 10.1109/ISWC.2005.22

M. Ros, M. Cuellar, M. Delgado, and A. Vila, Online recognition of human activities and adaptation to habit changes by means of learning automata and fuzzy temporal windows, Information Sciences, vol.220, pp.86-101, 2013.
DOI : 10.1016/j.ins.2011.10.005

M. Sköldstam, K. Akesson, and M. Fabian, Modeling of discrete event systems using finite automata with variables, 2007 46th IEEE Conference on Decision and Control, pp.3387-3392, 2007.
DOI : 10.1109/CDC.2007.4434894

X. Yu, X. Wang, P. Kittipanya-ngam, H. Eng, C. et al., Fall detection and alert for ageingat-home of elderly, Proc. of the 7th International Conference on Smart Homes and Health Telematics (ICOST'09), pp.209-216, 2009.