A sensory grammar for inferring behaviors in sensor networks
Proceedings of the 5th international conference on Information processing in sensor networks
Address-event imagers for sensor networks: evaluation and modeling
Proceedings of the 5th international conference on Information processing in sensor networks
How smart are our environments? An updated look at the state of the art
Pervasive and Mobile Computing
Data visualisation and data mining technology for supporting care for older people
Proceedings of the 9th international ACM SIGACCESS conference on Computers and accessibility
Detecting Patterns for Assisted Living Using Sensor Networks: A Case Study
SENSORCOMM '07 Proceedings of the 2007 International Conference on Sensor Technologies and Applications
Extracting spatiotemporal human activity patterns in assisted living using a home sensor network
Proceedings of the 1st international conference on PErvasive Technologies Related to Assistive Environments
STFL: a spatio temporal filtering language with applications in assisted living
Proceedings of the 2nd International Conference on PErvasive Technologies Related to Assistive Environments
Reflecting on pills and phone use: supporting awareness of functional abilities for older adults
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
The User's Touch: A Design Requirement for Smart Spaces
International Journal of Advanced Pervasive and Ubiquitous Computing
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The in-house monitoring of elders using intelligent sensors is a very desirable service that has the potential of increasing autonomy and independence while minimizing the risks of living alone. Because of this promise, the efforts of building such systems have been spanning for decades, but there is still a lot of room for improvement. Driven by the recent technology advances in many of the required components, in this paper we present a scalable framework for detailed behavior interpretation of elders. We report on our early deployment experiences and present our current progress in three main components: sensors, middleware and behavior interpretation mechanisms that aim to make effective monitoring and assistive services a reality.