Coping with multiple residents in a smart environment

Smart environment research has resulted in many useful tools for modeling, monitoring, and adapting to a single resident. However, many of these tools are not equipped for coping with multiple residents in the same environment simultaneously. In this paper we investigate a first step in coping with multiple residents, that of attributing sensor events to individuals in a multi-resident environment. We discuss approaches that can be used to achieve this goal and we evaluate our implementations in the context of two physical smart environment testbeds. We also explore how learning resident identifiers can aid in performing other analyses on smart environment sensor data such as activity recognition.

[1]  Stephen S. Intille,et al.  Designing a Home of the Future , 2002, IEEE Pervasive Comput..

[2]  Sajal K. Das,et al.  A cooperative learning framework for mobility-aware resource management in multi-inhabitant smart homes , 2005, The Second Annual International Conference on Mobile and Ubiquitous Systems: Networking and Services.

[3]  Qiang Yang,et al.  Activity recognition via user-trace segmentation , 2008, TOSN.

[4]  Diane J. Cook,et al.  Knowledge Discovery in Entity Based Smart Environment Resident Data Using Temporal Relation Based Data Mining , 2007, Seventh IEEE International Conference on Data Mining Workshops (ICDMW 2007).

[5]  Andrew J. Viterbi,et al.  Error bounds for convolutional codes and an asymptotically optimum decoding algorithm , 1967, IEEE Trans. Inf. Theory.

[6]  Calton Pu,et al.  Moving Towards Massively Scalable Video-Based Sensor Networks , 2001 .

[7]  Christian Micheloni,et al.  Video security for ambient intelligence , 2005, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[8]  Diane J. Cook,et al.  Online Sequential Prediction via Incremental Parsing: The Active LeZi Algorithm , 2007, IEEE Intelligent Systems.

[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]  Diane J. Cook,et al.  How smart are our environments? An updated look at the state of the art , 2007, Pervasive Mob. Comput..

[11]  Diane J. Cook,et al.  Using Temporal Relations in Smart Environment Data for Activity Prediction , 2007 .

[12]  YangQiang,et al.  Activity recognition via user-trace segmentation , 2008 .

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

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

[15]  Paramvir Bahl,et al.  RADAR: an in-building RF-based user location and tracking system , 2000, Proceedings IEEE INFOCOM 2000. Conference on Computer Communications. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (Cat. No.00CH37064).

[16]  Svetha Venkatesh,et al.  Multi-modal emotive computing in a smart house environment , 2007, Pervasive Mob. Comput..

[17]  Diane J. Cook,et al.  MINING TEMPORAL SEQUENCES TO DISCOVER INTERESTING PATTERNS , 2004 .

[18]  M. Skubic,et al.  Findings from a participatory evaluation of a smart home application for older adults. , 2008, Technology and health care : official journal of the European Society for Engineering and Medicine.

[19]  J. Krumm,et al.  Multi-camera multi-person tracking for EasyLiving , 2000, Proceedings Third IEEE International Workshop on Visual Surveillance.

[20]  Emmanuel Munguia Tapia,et al.  Toward Scalable Activity Recognition for Sensor Networks , 2006, LoCA.

[21]  Gaetano Borriello,et al.  Location Systems for Ubiquitous Computing , 2001, Computer.

[22]  Li-Chen Fu,et al.  Creating Robust Activity Maps Using Wireless Sensor Network in a Smart Home , 2007, 2007 IEEE International Conference on Automation Science and Engineering.

[23]  Hari Balakrishnan,et al.  6th ACM/IEEE International Conference on on Mobile Computing and Networking (ACM MOBICOM ’00) The Cricket Location-Support System , 2022 .

[24]  Diane J Cook,et al.  Assessing the Quality of Activities in a Smart Environment , 2009, Methods of Information in Medicine.

[25]  Gregory D. Abowd,et al.  The smart floor: a mechanism for natural user identification and tracking , 2000, CHI Extended Abstracts.

[26]  B. Kröse,et al.  Bayesian Activity Recognition in Residence for Elders , 2007 .

[27]  W. Eric L. Grimson,et al.  Learning Patterns of Activity Using Real-Time Tracking , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[28]  Diane J. Cook,et al.  Inhabitant Guidance of Smart Environments , 2007, HCI.