Determination of Activities of Daily Living of independent living older people using environmentally placed sensors

The rapid increase in the ageing population of most developed countries is presenting significant challenges to policymakers of public healthcare. To address this problem, we propose a Smarter Safer Home solution that enables ageing Australians to live independently longer in their own homes. The primary aim of our approach is to enhance the Quality of Life (QoL) of aged citizens and the Family Quality of Life (FQoL) for the adult children supporting their aged parents. To achieve this, we use environmentally placed sensors for non-intrusive monitoring of human behaviour. The various sensors will detect and gather activity and ambience data which will be fused through specific decision support algorithms to extract Activities of Daily Living (ADLs). Subsequently, these estimated ADLs would be correlated with reported and recorded health events to predicate health decline or critical health situations from the changes in ADLs.

[1]  Gary Banks,et al.  Health costs and policy in an ageing Australia 1 , 2008 .

[2]  Susumu Tachi,et al.  Pervasive Sensor System for Evidence-based Nursing Care Support , 2006, ICRA.

[3]  François Brémond,et al.  Monitoring Activities of Daily Living (ADLs) of Elderly Based on 3D Key Human Postures , 2009, ICVW.

[4]  Deborah Estrin,et al.  An energy-efficient MAC protocol for wireless sensor networks , 2002, Proceedings.Twenty-First Annual Joint Conference of the IEEE Computer and Communications Societies.

[5]  Koen Langendoen,et al.  An adaptive energy-efficient MAC protocol for wireless sensor networks , 2003, SenSys '03.

[6]  Rama Chellappa,et al.  Machine Recognition of Human Activities: A Survey , 2008, IEEE Transactions on Circuits and Systems for Video Technology.

[7]  R.S. Sangwan,et al.  Using RFID tags for tracking patients, charts and medical equipment within an integrated health delivery network , 2005, Proceedings. 2005 IEEE Networking, Sensing and Control, 2005..

[8]  Wayne Swan,et al.  Australia to 2050: future challenges , 2010 .

[9]  Henry A. Kautz,et al.  An Overview of the Assisted Cognition Project , 2002 .

[10]  Nir Friedman,et al.  Probabilistic Graphical Models - Principles and Techniques , 2009 .

[11]  Marta Marron,et al.  Visual fall detection for intelligent spaces , 2009, 2009 IEEE International Symposium on Intelligent Signal Processing.

[12]  M. Lakshmanan,et al.  AN ADAPTIVE ENERGY EFFICIENT MAC PROTOCOL FOR WIRELESS SENSOR NETWORKS , 2009 .

[13]  Svetha Venkatesh,et al.  Activity recognition and abnormality detection with the switching hidden semi-Markov model , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[14]  David J. Fleet,et al.  This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE Gaussian Process Dynamical Model , 2007 .

[15]  Gwenn Englebienne,et al.  Accurate activity recognition in a home setting , 2008, UbiComp.

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

[17]  Albert Ali Salah,et al.  Computer Analysis of Human Behavior , 2011 .

[18]  Abdenour Bouzouane,et al.  A KEYHOLE PLAN RECOGNITION MODEL FOR ALZHEIMER'S PATIENTS: FIRST RESULTS , 2007, Appl. Artif. Intell..

[19]  Sydney Katz.  Assessing Self‐maintenance: Activities of Daily Living, Mobility, and Instrumental Activities of Daily Living , 1983, Journal of the American Geriatrics Society.

[20]  T. Ferguson.  A Bayesian Analysis of Some Nonparametric Problems , 1973 .