Inferring Activities from Interactions with Objects
IEEE Pervasive Computing
Battery-free Wireless Identification and Sensing
IEEE Pervasive Computing
Unsupervised activity recognition using automatically mined common sense
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
Activity recognition from accelerometer data
IAAI'05 Proceedings of the 17th conference on Innovative applications of artificial intelligence - Volume 3
Common sense based joint training of human activity recognizers
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Modeling and intelligibility in ambient environments
Journal of Ambient Intelligence and Smart Environments
Recognising activities of daily life using hierarchical plans
EuroSSC'07 Proceedings of the 2nd European conference on Smart sensing and context
Building reliable activity models using hierarchical shrinkage and mined ontology
PERVASIVE'06 Proceedings of the 4th international conference on Pervasive Computing
Testing asbru guidelines and protocols for neonatal intensive care
AIME'05 Proceedings of the 10th conference on Artificial Intelligence in Medicine
Capacitive indoor positioning and contact sensing for activity recognition in smart homes
Journal of Ambient Intelligence and Smart Environments
Motion detection with pyramid structure of background model for intelligent surveillance systems
Engineering Applications of Artificial Intelligence
Home-based health monitoring of the elderly through gait recognition
Journal of Ambient Intelligence and Smart Environments - Home-based Health and Wellness Measurement and Monitoring
Mobile application usage prediction through context-based learning
Journal of Ambient Intelligence and Smart Environments
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Smart environments are emerging as platforms that can be used to help recognise activities and hence provide context sensitive services and assistance, e.g. switching on the music while the person being monitored is having an evening meal. The ability to monitor everyday activities in a smart environment is seen as a key approach for tracking functional decline among elderly people. The motivation is to allow patients with early Alzheimer's disease to have additional years of independent living before the disease reaches the latter stages (moderate and severe). This paper describes an approach to detecting the goals of the individual subjects from sensor data that are generated by objects that are used when performing everyday activities around the home. To limit intrusion into personal privacy cameras and visual surveillance equipment are not used, as the activities are monitored using simple RFID sensors. Identification of the intentions of subjects is based on interpretation of the sensor data exploiting known structures of typical behaviours.