Reid Priedhorsky, David Pitchford, et al.
CSCW 2012
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%. © 2013 ACM 1073-0516/2013/12-ART32.
Reid Priedhorsky, David Pitchford, et al.
CSCW 2012
Arup Acharya, Nilankan Banerjee, et al.
IEEE Pervasive Computing
David Piorkowski, Inge Vejsbjerg, et al.
PACM HCI
Rajesh Balchandran, Leonid Rachevsky, et al.
INTERSPEECH 2009