MONITORING BEHAVIOR WITH AN ARRAY OF SENSORS

The objective is to detect activities taking place in a home and to create a model of behavior for the occupant. A behavior is a pattern in the sequence of activities. An array of sensors captures the status of appliances. Models for the occupant's activities are built from the captured data using supervised and unsupervised learning techniques. The models of behavior are built using the hidden Markov model (HMM) technique. Predictive models can be used in a number of ways: to enhance user experience, to maximize resource usage efficiency, for safety and security. This work focuses on supporting independent living and enhancing quality of life of older persons. The ultimate goal is for the system to distinguish between normal and anomalous behavior. In this paper, we present the results of comparing supervised and unsupervised classification techniques applied to the problem of modeling activity for the purpose of modeling behavior in a home.

[1]  Barry Brumitt,et al.  EasyLiving: Technologies for Intelligent Environments , 2000, HUC.

[2]  Diane J. Cook,et al.  PREDIcting inhabitant action using action and task models with application to smart homes , 2004, Int. J. Artif. Intell. Tools.

[3]  Oliver Brdiczka,et al.  Learning to detect user activity and availability from a variety of sensor data , 2004, Second IEEE Annual Conference on Pervasive Computing and Communications, 2004. Proceedings of the.

[4]  J. MacQueen.  Some methods for classification and analysis of multivariate observations , 1967 .

[5]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[6]  J. Ross Quinlan,et al.  Improved Use of Continuous Attributes in C4.5 , 1996, J. Artif. Intell. Res..

[7]  Pavel Berkhin,et al.  A Survey of Clustering Data Mining Techniques , 2006, Grouping Multidimensional Data.

[8]  Diane J. Cook,et al.  Smart environments - technology, protocols and applications , 2004 .

[9]  Martha E. Pollack,et al.  Intelligent Technology for an Aging Population: The Use of AI to Assist Elders with Cognitive Impairment , 2005, AI Mag..

[10]  Anil K. Jain,et al.  Statistical Pattern Recognition: A Review , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  William W. Cohen.  Fast Effective Rule Induction , 1995, ICML.

[12]  Robert L. Grossman,et al.  Data Mining for Scientific and Engineering Applications , 2001, Massive Computing.

[13]  Kent Larson,et al.  Activity Recognition in the Home Using Simple and Ubiquitous Sensors , 2004, Pervasive.

[14]  Hani Hagras,et al.  A neural network agent based approach to activity detection in AmI environments , 2005 .

[15]  Hani Hagras,et al.  An Intelligent Fuzzy Agent Approach for Realising Ambient Intelligence in Intelligent Inhabited Environments , 2005 .

[16]  Hani Hagras,et al.  A fuzzy embedded agent-based approach for realizing ambient intelligence in intelligent inhabited environments , 2005, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[17]  Finn V. Jensen,et al.  Bayesian Networks and Decision Graphs , 2001, Statistics for Engineering and Information Science.

[18]  Oliver Brdiczka,et al.  Unsupervised Segmentation of Meeting Configurations and Activities using Speech Activity Detection , 2006, AIAI.

[19]  J. R. Landis,et al.  An application of hierarchical kappa-type statistics in the assessment of majority agreement among multiple observers. , 1977, Biometrics.

[20]  Emmanuel,et al.  Activity recognition in the home setting using simple and ubiquitous sensors , 2003 .

[21]  Diane J. Cook,et al.  Designing and modeling smart environments , 2006, 2006 International Symposium on a World of Wireless, Mobile and Multimedia Networks(WoWMoM'06).

[22]  Ferdinando Fornara,et al.  Elderly people at home: technological help in everyday activities , 2005, ROMAN 2005. IEEE International Workshop on Robot and Human Interactive Communication, 2005..