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Bien, Zeungnam
Intelligent Robot Control System Lab
Research Interests
  • Intelligent Control
  • Learning System Methodologies
  • Assistive Robotics
  • Smart Home System

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A Nonsupervised Learning Framework of Human Behavior Patterns Based on Sequential Actions

DC Field Value Language
dc.contributor.author Lee, Sang Wan -
dc.contributor.author Kim, Yong Soo -
dc.contributor.author Bien, Zeungnam -
dc.date.accessioned 2014-04-10T01:14:11Z -
dc.date.available 2014-04-10T01:14:11Z -
dc.date.created 2013-06-07 -
dc.date.issued 2010-04 -
dc.identifier.citation IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, v.22, no.4, pp.479 - 492 -
dc.identifier.issn 1041-4347 -
dc.identifier.uri https://scholarworks.unist.ac.kr/handle/201301/2947 -
dc.description.abstract In designing autonomous service systems such as assistive robots for the aged and the disabled, discovery and prediction of human actions are important and often crucial. Patterns of human behavior, however, involve ambiguity, uncertainty, complexity, and inconsistency caused by physical, logical, and emotional factors, and thus their modeling and recognition are known to be difficult. In this paper, a nonsupervised learning framework of human behavior patterns is suggested in consideration of human behavioral characteristics. Our approach consists of two steps. In the first step, a meaningful structure of data is discovered by using Agglomerative Iterative Bayesian Fuzzy Clustering (AIBFC) with a newly proposed cluster validity index. In the second step, the sequence of actions is learned on the basis of the structure discovered in the first step and by utilizing the proposed Fuzzy-state Q-learning (FSQL) process. These two learning steps are incorporated in an amalgamated framework, AIBFC-FSQL, which is capable of learning human behavior patterns in a nonsupervised manner and predicting subsequent human actions. Through a number of simulations with typical benchmark data sets, we show that the proposed learning method outperforms several well-known methods. We further conduct experiments with two challenging real-world databases to demonstrate its usefulness from a practical perspective. -
dc.description.statementofresponsibility open -
dc.language ENG -
dc.publisher IEEE COMPUTER SOC -
dc.subject Assistive -
dc.subject Bayesian -
dc.subject Behavioral characteristics -
dc.subject Benchmark data -
dc.subject Cluster validity indices -
dc.subject Emotional factors -
dc.subject Human actions -
dc.subject Human behavior -
dc.subject Human behaviors -
dc.subject Learning frameworks -
dc.subject Learning methods -
dc.subject Q-learning -
dc.subject Real-world database -
dc.subject Sequence of actions -
dc.subject Service systems -
dc.title A Nonsupervised Learning Framework of Human Behavior Patterns Based on Sequential Actions -
dc.type ARTICLE -
dc.identifier.scopusid 2-s2.0-77649267492 -
dc.identifier.wosid 000274654800002 -
dc.type.rims ART -
dc.description.wostc 7 *
dc.description.scopustc 7 *
dc.date.tcdate 2015-02-28 *
dc.date.scptcdate 2014-08-25 *
dc.identifier.doi 10.1109/TKDE.2009.123 -
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