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Complex activity recognition using context-driven activity theory and activity signatures

journal contribution
posted on 2013-12-01, 00:00 authored by Saguna, Arkady ZaslavskyArkady Zaslavsky, D Chakraborty
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%.

History

Journal

ACM transactions on computer-human interaction

Volume

20

Article number

32

Pagination

1-34

Location

New York, N.Y.

ISSN

1073-0516

eISSN

1557-7325

Language

eng

Publication classification

C1.1 Refereed article in a scholarly journal

Copyright notice

2013, ACM

Issue

6

Publisher

Association for Computing Machinery