Activity- and Inactivity-Based Approaches to Analyze an Assisted Living Environment
SECURWARE '08 Proceedings of the 2008 Second International Conference on Emerging Security Information, Systems and Technologies
A Predictive Analysis of the Night-Day Activities Level of Older Patient in a Health Smart Home
ICOST '09 Proceedings of the 7th International Conference on Smart Homes and Health Telematics: Ambient Assistive Health and Wellness Management in the Heart of the City
Using association rule mining to discover temporal relations of daily activities
ICOST'11 Proceedings of the 9th international conference on Toward useful services for elderly and people with disabilities: smart homes and health telematics
An ambient approach to emergency detection based on location tracking
ICOST'11 Proceedings of the 9th international conference on Toward useful services for elderly and people with disabilities: smart homes and health telematics
Abnormal behaviours identification for an elder's life activities using dissimilarity measurements
Proceedings of the 4th International Conference on PErvasive Technologies Related to Assistive Environments
Behavioral Patterns of Older Adults in Assisted Living
IEEE Transactions on Information Technology in Biomedicine
Detection of Abnormal Living Patterns for Elderly Living Alone Using Support Vector Data Description
IEEE Transactions on Information Technology in Biomedicine
IEEE Transactions on Information Technology in Biomedicine
Testing classifiers for embedded health assessment
ICOST'12 Proceedings of the 10th international smart homes and health telematics conference on Impact Ananlysis of Solutions for Chronic Disease Prevention and Management
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A technical system for unobtrusive presence measurement and two novel models for describing user behavior in domestic environments are presented. Within the developed models user behavior is either described as the probability of being present at a certain location within an environment at a certain time on a day of the week or being present at a location for a certain number of times with a certain duration. The models are called timeslot-based and duration-based. Both models have been applied to presence information gathered by a technical system using home automation sensors. The system was installed into two flats of older people during a field trial for eight months. Results of the experiment show that the two models can be applied to describe individual user behavior. The influence of data structure and model quality on the detection of anomalies and the generation of alarms is discussed. On the long-term, the approach aims at detecting cutbacks in self-care ability and changes in health state by autonomously learning typical user behavior from presence information in a spatial model and by detecting untypical behavior, called anomalies, and generating alarms for caretakers. Such automatic assessment of self-care ability and health state is required in order to meet the increased challenges imposed to the decreasing number of care personal during the progress of the demographic change.