Health and wellness monitoring through wearable and ambient sensors: exemplars from home-based care of elderly with mild dementia

Monitoring and timely intervention are extremely important in the continuous management of health and wellness among all segments of the population, but particularly among those with mild dementia. In relation to this, we prescribe three design principles for the construction of services and applications. These are ambient intelligence, service continuity, and micro-context. In this paper, we provide three exemplars from our research and development activities that illustrate the use of these design principles in the construction of services and applications. All the applications are drawn from the field of care for mild dementia patients in their living quarters.

[1]  Thomas Pederson,et al.  easyADL - Wearable Support System for Independent Life despite Dementia , 2006, CHI 2006.

[2]  Wendong Xiao,et al.  An Extensible System for Sleep Activity Pattern Monitoring , 2007, 2007 3rd International Conference on Intelligent Sensors, Sensor Networks and Information.

[3]  Xingshe Zhou,et al.  Towards a Task Supporting System with CBR Approach in Smart Home , 2009, ICOST.

[4]  Donald E. Brown,et al.  A Rule-Based Approach to the Analysis of Elders' Activity Data: Detection of Health and Possible Emergency Conditions , 2005, AAAI Fall Symposium: Caring Machines.

[5]  Aung Aung Phyo Wai,et al.  Smart wireless continence management system for persons with dementia. , 2008, Telemedicine journal and e-health : the official journal of the American Telemedicine Association.

[6]  B. Reisberg.  Functional assessment staging (FAST). , 1988, Psychopharmacology bulletin.

[7]  Chen-Khong Tham,et al.  Information quality mapping in resource-constrained multi-modal data fusion system over wireless sensor network with losses , 2007, 2007 6th International Conference on Information, Communications & Signal Processing.

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

[9]  Felix Naumann,et al.  Assessing the Completeness of Sensor Data , 2006, DASFAA.

[10]  Andrei Tolstikov,et al.  Model and algorithmic framework for detection and correction of cognitive errors. , 2009, Technology and health care : official journal of the European Society for Engineering and Medicine.

[11]  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.

[12]  David E. Culler,et al.  Mica: A Wireless Platform for Deeply Embedded Networks , 2002, IEEE Micro.

[13]  Chen-Khong Tham,et al.  Data loss regulation to ensure information quality in sensor networks , 2005, 2005 International Conference on Intelligent Sensors, Sensor Networks and Information Processing.

[14]  Jesse Hoey.  Tracking using Flocks of Features, with Application to Assisted Handwashing , 2006, BMVC.

[15]  C. Atkeson,et al.  Toward the Automatic Assessment of Behavioral Distrubances of Dementia , 2003 .