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Complex activity recognition using context-driven activity theory and activity signatures
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science. Monash University, Melbourne, Australia.ORCID iD: 0000-0001-8561-7963
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science. CSIRO, Acton, ACT, Australia.ORCID iD: 0000-0003-1990-5734
IBM Research, New Delhi, India.
2013 (English)In: ACM Transactions on Computer-Human Interaction, ISSN 1073-0516, E-ISSN 1557-7325, Vol. 20, no 6, article id 32Article in journal (Refereed) Published
Abstract [en]

In pervasive and ubiquitous computing systems, human activity recognition has immense potential in a large number of application domains. Current activity recognition techniques (i) do not handle variations in sequence, concurrency and interleaving of complex activities; (ii) do not incorporate context; and (iii) require large amounts of training data. There is a lack of a unifying theoretical framework which exploits both domain knowledge and data-driven observations to infer complex activities. In this article, we propose, develop and validate a novel Context-Driven Activity Theory (CDAT) for recognizing complex activities. We develop a mechanism using probabilistic and Markov chain analysis to discover complex activity signatures and generate complex activity definitions. We also develop a Complex Activity Recognition (CAR) algorithm. It achieves an overall accuracy of 95.73% using extensive experimentation with real-life test data. CDAT utilizes context and links complex activities to situations, which reduces inference time by 32.5% and also reduces training data by 66%.

Place, publisher, year, edition, pages
Association for Computing Machinery (ACM), 2013. Vol. 20, no 6, article id 32
Keywords [en]
Activity recognition, complex activity, context-driven activity the- ory, context-awareness, concurrent activities, interleaved activities, prototype, test bed, experimentation, evaluation
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:ltu:diva-102273DOI: 10.1145/2490832ISI: 000330746900001Scopus ID: 2-s2.0-84892882036OAI: oai:DiVA.org:ltu-102273DiVA, id: diva2:1809528
Available from: 2023-11-03 Created: 2023-11-03 Last updated: 2024-03-09Bibliographically approved

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Saguna, SagunaZaslavsky, Arkady

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