Pervasive behavior tracking for cognitive assistance
Proceedings of the 1st international conference on PErvasive Technologies Related to Assistive Environments
Evidential fusion of sensor data for activity recognition in smart homes
Pervasive and Mobile Computing
Automatic extraction of clusters from hierarchical clustering representations
PAKDD'03 Proceedings of the 7th Pacific-Asia conference on Advances in knowledge discovery and data mining
Incorporating duration information in activity recognition
KSEM'10 Proceedings of the 4th international conference on Knowledge science, engineering and management
Using association rule mining to discover temporal relations of daily activities
ICOST'11 Proceedings of the 9th international conference on Toward useful services for elderly and people with disabilities: smart homes and health telematics
Using model-based clustering to discretise duration information for activity recognition
CBMS '11 Proceedings of the 2011 24th International Symposium on Computer-Based Medical Systems
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Activity recognition has become a key component within smart environments that aim at providing assistive solutions for their users. Learning high level activities from low level sensor data depends on several parameters, one of which is the duration of the activities themselves. Nevertheless, directly incorporating continuous duration values into a model is a complex process and may not prove to be very qualitative. In this paper we aim at discretising activity related durations using different clustering algorithms. We explore the possibility of discretising duration data through the use of rudimentary clustering algorithms such as visual inspection to more established methods such as model based clustering. In addition, a probabilistic model is built that predicts both person and activities from the observed values of sensor sequence, time and discrete duration values. Each of the models created is compared in terms of its performance in the prediction of activities. Following analysis of the results attained it has been found that irrespective of the clustering algorithm used for duration discretisation, incorporating the duration information increases the prediction performance. Prediction accuracy was improved by almost 3% when the model was built incorporating durations.