Modelling of Behavioural Patterns for Abnormality Detection in the Context of Lifestyle Reassurance
CIARP '08 Proceedings of the 13th Iberoamerican congress on Pattern Recognition: Progress in Pattern Recognition, Image Analysis and Applications
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A Wireless Sensor Network for Assisted Living at Home of Elderly People
IWINAC '09 Proceedings of the 3rd International Work-Conference on The Interplay Between Natural and Artificial Computation: Part II: Bioinspired Applications in Artificial and Natural Computation
Fast track article: Assessing everyday life behavioral rhythms for the older generation
Pervasive and Mobile Computing
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Expert Systems with Applications: An International Journal
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ACM Transactions on Management Information Systems (TMIS)
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PERSUASIVE'12 Proceedings of the 7th international conference on Persuasive Technology: design for health and safety
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
A home daily activity simulation model for the evaluation of lifestyle monitoring systems
Computers in Biology and Medicine
Longitudinal residential ambient monitoring: correlating sensor data to functional health status
Proceedings of the 7th International Conference on Pervasive Computing Technologies for Healthcare
Proceedings of the 7th International Conference on Pervasive Computing Technologies for Healthcare
Modeling individual healthy behavior using home automation sensor data: Results from a field trial
Journal of Ambient Intelligence and Smart Environments - Design and Deployment of Intelligent Environments
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In this paper, we examine at-home activity rhythms and present a dozen of behavioral patterns obtained from an activity monitoring pilot study of 22 residents in an assisted living setting with four case studies. Established behavioral patterns have been captured using custom software based on a statistical predictive algorithm that models circadian activity rhythms (CARs) and their deviations. The CAR was statistically estimated based on the average amount of time a resident spent in each room within their assisted living apartment, and also on the activity level given by the average n.umber of motion events per room. A validated in-home monitoring system (IMS) recorded the monitored resident's movement data and established the occupancy period and activity level for each room. Using these data, residents' circadian behaviors were extracted, deviations indicating anomalies were detected, and the latter were correlated to activity reports generated by the IMS as well as notes of the facility's professional caregivers on the monitored residents. The system could be used to detect deviations in activity patterns and to warn caregivers of such deviations, which could reflect changes in health status, thus providing caregivers with the opportunity to apply standard of care diagnostics and to intervene in a timely manner.