Paper Info
Title
Complex activity recognition using context-driven activity theory and activity signatures
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%.
Year
DOI
Venue
2013
10.1145/2490832
ACM Trans. Comput.-Hum. Interact.
Keywords
Field
DocType
test bed,evaluation,activity recognition,prototype
Activity recognition,Domain knowledge,Concurrency,Computer science,Inference,Markov chain,Context awareness,Human–computer interaction,Artificial intelligence,Ubiquitous computing,Probabilistic logic,Machine learning
Journal
Volume
Issue
ISSN
20
6
1073-0516
Citations 
PageRank 
References 
21
0.89
51
Authors
3
Name
Order
Citations
PageRank
Saguna1515.28
Arkady B. Zaslavsky2943168.27
Dipanjan Chakraborty31761118.69