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

  • Authors:
  • Saguna Saguna;Arkady Zaslavsky;Dipanjan Chakraborty

  • Affiliations:
  • Monash University and Luleå University of Technology, Luleå, Sweden;CSIRO and Luleå University of Technology, Luleå, Sweden;IBM Research, New Delhi, India

  • Venue:
  • ACM Transactions on Computer-Human Interaction (TOCHI)
  • Year:
  • 2013

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Abstract

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%.