Tracking Activities in Complex Settings Using Smart Environment Technologies.

The pervasive sensing technologies found in smart homes offer unprecedented opportunities for providing health monitoring and assistance to individuals experiencing difficulties living independently at home. A primary challenge that needs to be tackled to meet this need is the ability to recognize and track functional activities that people perform in their own homes and everyday settings. In this paper we look at approaches to perform real-time recognition of Activities of Daily Living. We enhance other related research efforts to develop approaches that are effective when activities are interrupted and interleaved. To evaluate the accuracy of our recognition algorithms we assess them using real data collected from participants performing activities in our on-campus smart apartment testbed.

[1]  Henry A. Kautz,et al.  Location-Based Activity Recognition using Relational Markov Networks , 2005, IJCAI.

[2]  Jesús Favela,et al.  Activity Recognition for the Smart Hospital , 2008, IEEE Intelligent Systems.

[3]  Vincent Rialle,et al.  What Do Family Caregivers of Alzheimer’s Disease Patients Desire in Smart Home Technologies? , 2009, Methods of Information in Medicine.

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

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

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

[7]  M. Schmitter-Edgecombe,et al.  Characterizing multiple memory deficits and their relation to everyday functioning in individuals with mild cognitive impairment. , 2009, Neuropsychology.

[8]  J. Barbenel,et al.  The efficacy of an intelligent cognitive orthosis to facilitate handwashing by persons with moderate to severe dementia , 2004 .

[9]  B. Reisberg,et al.  The Alzheimer's Disease Activities of Daily Living International Scale (ADL-IS) , 2001, International Psychogeriatrics.

[10]  Van Nostrand,et al.  Error Bounds for Convolutional Codes and an Asymptotically Optimum Decoding Algorithm , 1967 .

[11]  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.

[12]  Daniel P. Siewiorek,et al.  Activity recognition and monitoring using multiple sensors on different body positions , 2006, International Workshop on Wearable and Implantable Body Sensor Networks (BSN'06).

[13]  O. Okonkwo,et al.  Mild cognitive impairment and everyday function: evidence of reduced speed in performing instrumental activities of daily living. , 2008, The American journal of geriatric psychiatry : official journal of the American Association for Geriatric Psychiatry.

[14]  D. M. Hutton,et al.  Smart Environments: Technology, Protocols and Applications , 2005 .

[15]  Ozioma C. Okonkwo,et al.  Mild cognitive impairment and everyday function: evidence of reduced speed in performing instrumental activities of daily living. , 2008 .