Ea-Ee Jan, Hong-Kwang Kuo, et al.
INTERSPEECH 2009
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.
Ea-Ee Jan, Hong-Kwang Kuo, et al.
INTERSPEECH 2009
Charles Wieeha, Pedro Szekely
CHI EA 2001
Noi Sukaviriya, Rick Kjeldsen, et al.
CHI EA 2004
Oznur Alkan, Massimilliano Mattetti, et al.
INFORMS 2020