Dining Activity Analysis Using a Hidden Markov Model
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Inferring Activities from Interactions with Objects
IEEE Pervasive Computing
Multi-modal emotive computing in a smart house environment
Pervasive and Mobile Computing
Location estimation in a smart home: system implementation and evaluation using experimental data
International Journal of Telemedicine and Applications - Pervasive Health Care Services and Technologies
Pervasive behavior tracking for cognitive assistance
Proceedings of the 1st international conference on PErvasive Technologies Related to Assistive Environments
Decision Support for Alzheimer's Patients in Smart Homes
CBMS '08 Proceedings of the 2008 21st IEEE International Symposium on Computer-Based Medical Systems
Evidential fusion of sensor data for activity recognition in smart homes
Pervasive and Mobile Computing
On using temporal features to create more accurate human-activity classifiers
AICS'09 Proceedings of the 20th Irish conference on Artificial intelligence and cognitive science
Duration discretisation for activity recognition
Technology and Health Care
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Activity recognition has become a key issue in smart home environments. The problem involves learning high level activities from low level sensor data. Activity recognition can depend on several variables; one such variable is duration of engagement with sensorised items or duration of intervals between sensor activations that can provide useful information about personal behaviour. In this paper a probabilistic learning algorithm is proposed that incorporates episode, time and duration information to determine inhabitant identity and the activity being undertaken from low level sensor data. Our results verify that incorporating duration information consistently improves the accuracy.