Support for Context-aware Monitoring in Home Healthcare

This paper tackles the problem of supporting independent living and well-being for people that live in their homes and have no critical chronic condition. The paper assumes the presence of a monitoring system equipped with a pervasive sensor network and a non-monotonic reasoning engine. The rich set of sensors that can be used for monitoring in home environments and their sheer number make it quite complex to provide a correct interpretation of collected data for a particular patient. For this reason, we introduce a logic-based context model for situation assessment combined with high level declarative feedback policy specification, and we use logic programming techniques to reason about different pieces of knowledge for prevention.

[1]  Tamara Tse,et al.  The environment and falls prevention: Do environmental modifications make a difference? , 2005 .

[2]  Wolfgang Faber,et al.  The DLV system for knowledge representation and reasoning , 2002, TOCL.

[3]  Timo Soininen,et al.  Extending and implementing the stable model semantics , 2000, Artif. Intell..

[4]  M. Tinetti Performance‐Oriented Assessment of Mobility Problems in Elderly Patients , 1986, Journal of the American Geriatrics Society.

[5]  A. Stuck,et al.  Risk factors for functional status decline in community-living elderly people: a systematic literature review. , 1999, Social science & medicine.

[6]  I. Niemelä,et al.  Extending the Smodels system with cardinality and weight constraints , 2001 .

[7]  Bill N. Schilit,et al.  Context-aware computing applications , 1994, Workshop on Mobile Computing Systems and Applications.

[8]  Martha E. Pollack,et al.  Adaptive cognitive orthotics: combining reinforcement learning and constraint-based temporal reasoning , 2004, ICML.

[9]  Henry Kautz,et al.  Behavior recognition in assisted cognition , 2004, AAAI 2004.

[10]  Claudia Linnhoff-Popien,et al.  A Context Modeling Survey , 2004 .

[11]  Ilkka Niemelä,et al.  Implementing Ordered Disjunction Using Answer Set Solvers for Normal Programs , 2002, JELIA.

[12]  R Bisiani,et al.  Context-aware Prediction and Prevention to Extend Healthy Life Years: Preventing Falls , 2009, IJCAI 2009.

[13]  H. Yanco,et al.  Automation as Caregiver: A Survey of Issues and Technologies , 2003 .

[14]  Roy H. Campbell,et al.  An infrastructure for context-awareness based on first order logic , 2003, Personal and Ubiquitous Computing.

[15]  Martin Gebser,et al.  GrinGo : A New Grounder for Answer Set Programming , 2007, LPNMR.

[16]  S. Boonen,et al.  Costs and consequences of hip fracture occurrence in old age: An economic perspective , 2005, Disability and rehabilitation.

[17]  Diane Podsiadlo,et al.  The Timed “Up & Go”: A Test of Basic Functional Mobility for Frail Elderly Persons , 1991, Journal of the American Geriatrics Society.

[18]  Tao Gu,et al.  Ontology based context modeling and reasoning using OWL , 2004, IEEE Annual Conference on Pervasive Computing and Communications Workshops, 2004. Proceedings of the Second.

[19]  Michael Gelfond,et al.  Integrating answer set programming and constraint logic programming , 2008, Annals of Mathematics and Artificial Intelligence.

[20]  Torsten Schaub,et al.  Qualitative Constraint Enforcement in Advanced Policy Specification , 2007, ECSQARU.

[21]  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.

[22]  Alessandra Mileo,et al.  A Logical Approach to Home Healthcare with Intelligent Sensor-Network Support , 2010, Comput. J..

[23]  S L Wolf,et al.  Environmental and behavioral circumstances associated with falls at home among healthy elderly individuals. Atlanta FICSIT Group. , 1997, Archives of physical medicine and rehabilitation.

[24]  Martin Gebser,et al.  clasp : A Conflict-Driven Answer Set Solver , 2007, LPNMR.

[25]  L. Yardley,et al.  Development and initial validation of the Falls Efficacy Scale-International (FES-I). , 2005, Age and ageing.

[26]  Martha E. Pollack,et al.  Evaluating user preferences for adaptive reminding , 2008, CHI Extended Abstracts.

[27]  J. Evans,et al.  Practical functional assessment of elderly persons: a primary-care approach. , 1995, Mayo Clinic proceedings.

[28]  Elisa Bertino,et al.  PDL with preferences , 2005, Sixth IEEE International Workshop on Policies for Distributed Systems and Networks (POLICY'05).

[29]  David R. Morse,et al.  Enhanced Reality Fieldwork: the Context Aware Archaeological Assistant , 1997 .

[30]  K. Ness,et al.  Screening, education, and associated behavioral responses to reduce risk for falls among people over age 65 years attending a community health fair. , 2003, Physical therapy.

[31]  Chiaki Sakama,et al.  An alternative approach to the semantics of disjunctive logic programs and deductive databases , 2004, Journal of Automated Reasoning.

[32]  Matthias Baldauf,et al.  A survey on context-aware systems , 2007, Int. J. Ad Hoc Ubiquitous Comput..

[33]  Sylvain Giroux,et al.  Semantic Matching Framework for handicap situation detection in smart environments , 2009, J. Ambient Intell. Smart Environ..

[34]  Gerhard Brewka,et al.  Logic programming with ordered disjunction , 2002, NMR.

[35]  Martha E. Pollack,et al.  Autominder: an intelligent cognitive orthotic system for people with memory impairment , 2003, Robotics Auton. Syst..

[36]  Allen Y. Yang,et al.  Distributed recognition of human actions using wearable motion sensor networks , 2009, J. Ambient Intell. Smart Environ..

[37]  L. Rubenstein Falls in older people: epidemiology, risk factors and strategies for prevention. , 2006, Age and ageing.