Pervasive and Mobile Computing
Probabilistic event calculus based on Markov logic networks
RuleML'11 Proceedings of the 5th international conference on Rule-based modeling and computing on the semantic web
Using constraint optimization for conflict resolution and detail control in activity recognition
AmI'11 Proceedings of the Second international conference on Ambient Intelligence
Event processing under uncertainty
Proceedings of the 6th ACM International Conference on Distributed Event-Based Systems
A constraint-based approach for proactive, context-aware human support
Journal of Ambient Intelligence and Smart Environments
Pervasive and Mobile Computing
Complex activity recognition using context-driven activity theory and activity signatures
ACM Transactions on Computer-Human Interaction (TOCHI)
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A majority of the approaches to activity recognition in sensor environments are either based on manually constructed rules for recognizing activities or lack the ability to incorporate complex temporal dependencies. Furthermore, in many cases, the rather unrealistic assumption is made that the subject carries out only one activity at a time. In this paper, we describe the use of Markov logic as a declarative framework for recognizing interleaved and concurrent activities incorporating both input from pervasive light-weight sensor technology and common-sense background knowledge. In particular, we assess its ability to learn statistical-temporal models from training data and to combine these models with background knowledge to improve the overall recognition accuracy. To this end, we propose two Markov logic formulations for inferring the foreground activity as well as each activities' start and end times. We evaluate the approach on an established dataset. where it outperforms state-of-the-art algorithms for activity recognition.