Mining models of human activities from the web
Proceedings of the 13th international conference on World Wide Web
Accurate activity recognition in a home setting
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
Assessment of the Impact of Sensor Failure in the Recognition of Activities of Daily Living
ICOST '08 Proceedings of the 6th international conference on Smart Homes and Health Telematics
Evidential fusion of sensor data for activity recognition in smart homes
Pervasive and Mobile Computing
Simultaneous tracking and activity recognition (STAR) using many anonymous, binary sensors
PERVASIVE'05 Proceedings of the Third international conference on Pervasive Computing
IEEE Transactions on Information Technology in Biomedicine
Ambient Assisted Living and Care in The Netherlands: The Voice of the User
International Journal of Ambient Computing and Intelligence
Segmenting sensor data for activity monitoring in smart environments
Personal and Ubiquitous Computing
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Automated recognition of activities of daily living such as preparing meals and grooming may be considered as one of the most desirable computational functions within a Smart Home for the elderly. In our current work we present a process framework with the capability of realising evidential ontology networks for recognising activities of daily living in a single-person occupied inhabitancy. The performance of this framework has been evaluated using a publicly available data set consisting of 28 days worth of sensor data which was recorded from a single person living in an apartment. Within the paper we show how evidential inference networks of activities of daily living can be generated from the smart home and subsequently used to represent sensor evidence and activity performance. Based on exposure to the data set considered within the study the model achieved an overall class accuracy of 83.4% and timeslice accuracy of 95.7%. Previously reported attempts to classify this data based on a probabilistic approach achieved rates in the region of 79.4% and 94.5% respectively.