A Possibilistic Approach for Activity Recognition in Smart Homes for Cognitive Assistance to Alzheimer’s Patients

Providing cognitive assistance to Alzheimer’s patients in smart homes is a field of research that receives a lot of attention lately. The recognition of the patient’s behavior when he carries out some activities in a smart home is primordial in order to give adequate assistance at the opportune moment. To address this challenging issue, we present a formal activity recognition framework based on possibility theory and description logics. We present initial results from an implementation of this recognition approach in a smart home laboratory.

[1]  Juan Carlos Augusto,et al.  Ambient Intelligence—the Next Step for Artificial Intelligence , 2008, IEEE Intelligent Systems.

[2]  Diego Calvanese,et al.  The Description Logic Handbook: Theory, Implementation, and Applications , 2003, Description Logic Handbook.

[3]  Diego Calvanese,et al.  The Description Logic Handbook , 2007 .

[4]  D. Cook,et al.  Smart Home-Based Health Platform for Behavioral Monitoring and Alteration of Diabetes Patients , 2009, Journal of diabetes science and technology.

[5]  Chen-Khong Tham,et al.  Eating activity primitives detection - a step towards ADL recognition , 2008, HealthCom 2008 - 10th International Conference on e-health Networking, Applications and Services.

[6]  Henry A. Kautz.  A formal theory of plan recognition , 1987 .

[7]  T. Sudkamp.  On probability-possibility transformations , 1992 .

[8]  Didier Dubois,et al.  Probability-Possibility Transformations, Triangular Fuzzy Sets, and Probabilistic Inequalities , 2004, Reliab. Comput..

[9]  Juan Carlos Augusto,et al.  Designing Smart Homes, The Role of Artificial Intelligence , 2006, Designing Smart Homes.

[10]  Stuart J. Russell,et al.  Dynamic bayesian networks: representation, inference and learning , 2002 .

[11]  Anand S. Rao,et al.  Means-End Plan Recognition - Towards a Theory of Reactive Recognition , 1994, KR.

[12]  Gal A. Kaminka,et al.  Incorporating Observer Biases in Keyhole Plan Recognition (Efficiently!) , 2007, AAAI.

[13]  Glenn Shafer,et al.  A Mathematical Theory of Evidence , 2020, A Mathematical Theory of Evidence.

[14]  Masoud Nikravesh,et al.  Forging New Frontiers: Fuzzy Pioneers II , 2007 .

[15]  Diane J. Cook,et al.  A Multi-agent Approach to Controlling a Smart Environment , 2006, Designing Smart Homes.

[16]  Ingrid Zukerman,et al.  Bayesian Models for Keyhole Plan Recognition in an Adventure Game , 2004, User Modeling and User-Adapted Interaction.

[17]  Didier Dubois,et al.  A possibility theory-based approach to the handling of uncertain relations between temporal points , 2004, Proceedings. 11th International Symposium on Temporal Representation and Reasoning, 2004. TIME 2004..

[18]  Henry A. Kautz,et al.  Fine-grained activity recognition by aggregating abstract object usage , 2005, Ninth IEEE International Symposium on Wearable Computers (ISWC'05).

[19]  Henry A. Kautz,et al.  Pervasive Computing in the Home and Community , 2006 .

[20]  B. Reisberg,et al.  The Global Deterioration Scale for assessment of primary degenerative dementia. , 1982, The American journal of psychiatry.

[21]  Jesse Hoey,et al.  The use of an intelligent prompting system for people with dementia , 2007, Interactions.

[22]  Vic Grout,et al.  User Modelling in Ambient Intelligence for Elderly and Disabled People , 2008, ICCHP.

[23]  R. Goldman,et al.  Partial Observability and Probabilistic Plan/Goal Recognition , 2005 .

[24]  S. Giroux,et al.  The Intelligent Habitat And Everyday Life Activity Support , 2003 .

[25]  Andrei Tolstikov,et al.  2-layer Erroneous-Plan Recognition for dementia patients in smart homes , 2009, 2009 11th International Conference on e-Health Networking, Applications and Services (Healthcom).

[26]  Chris D. Nugent,et al.  Evidential fusion of sensor data for activity recognition in smart homes , 2009, Pervasive Mob. Comput..

[27]  W. Lovejoy.  A survey of algorithmic methods for partially observed Markov decision processes , 1991 .

[28]  Abdenour Bouzouane,et al.  A hybrid plan recognition model for Alzheimer's patients: Interleaved-erroneous dilemma , 2009, Web Intell. Agent Syst..

[29]  William C. Mann,et al.  The Gator Tech Smart House: a programmable pervasive space , 2005, Computer.

[30]  Abdenour Bouzouane,et al.  The Praxis of Cognitive Assistance in Smart Homes , 2009, BMI Book.

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

[32]  Didier Dubois,et al.  Qualitative Possibility Theory in Information Processing , 2008 .

[33]  S Szewcyzk,et al.  Annotating smart environment sensor data for activity learning. , 2009, Technology and health care : official journal of the European Society for Engineering and Medicine.

[34]  Yarden Katz,et al.  Pellet: A practical OWL-DL reasoner , 2007, J. Web Semant..

[35]  Didier Dubois,et al.  Possibility Theory - An Approach to Computerized Processing of Uncertainty , 1988 .

[36]  Jesse Hoey,et al.  A planning system based on Markov decision processes to guide people with dementia through activities of daily living , 2006, IEEE Transactions on Information Technology in Biomedicine.

[37]  Chris D. Nugent,et al.  Semantic Smart Homes: Towards Knowledge Rich Assisted Living Environments , 2009 .

[38]  L. Zadeh.  Fuzzy sets as a basis for a theory of possibility , 1999 .

[39]  Context-Aware Computing,et al.  Inferring Activities from Interactions with Objects , 2004 .

[40]  Henry A. Kautz,et al.  Intelligent Ubiquitous Computing to Support Alzheimer ’ s Patients : Enabling the Cognitively Disabled , 2002 .

[41]  Mamoni Dhar.  Probability-Possibility Transformations : A Brief Revisit , 2012 .

[42]  Plan Recognition , 2010, Encyclopedia of Machine Learning.

[43]  A. Mihailidis,et al.  The COACH prompting system to assist older adults with dementia through handwashing: An efficacy study , 2008, BMC geriatrics.