Complex activity recognition using context driven activity theory in home environments

Authors:
Saguna Saguna;Arkady Zaslavsky;Dipanjan Chakraborty
Affiliations:
Monash University, Melbourne, Victoria, Australia and Lulea University of Technology, Lulea, Sweden;Monash University, Melbourne, Victoria, Australia and Lulea University of Technology, Lulea, Sweden;IBM Research, India Research Lab, New Delhi, India
Venue:
NEW2AN'11/ruSMART'11 Proceedings of the 11th international conference and 4th international conference on Smart spaces and next generation wired/wireless networking
Year:
2011

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Abstract

This paper proposes a context driven activity theory (CDAT) and reasoning approach for recognition of concurrent and interleaved complex activities of daily living (ADL) which involves no training and minimal annotation during the setup phase. We develop and validate our CDAT using the novel complex activity recognition algorithm on two users for three weeks. The algorithm accuracy reaches 88.5% for concurrent and interleaved activities. The inferencing of complex activities is performed online and mapped onto situations in near real-time mode. The developed systems performance is analyzed and its behavior evaluated.