BROWSE

Related Researcher

Author

Bien, Zeungnam
Intelligent Robot Control System Lab
Research Interests
  • Intelligent Control

ITEM VIEW & DOWNLOAD

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

Cited 7 times inthomson ciCited 7 times inthomson ci
Title
A Nonsupervised Learning Framework of Human Behavior Patterns Based on Sequential Actions
Author
Lee, Sang WanKim, Yong SooBien, Zeungnam
Keywords
Assistive;  Bayesian;  Behavioral characteristics;  Benchmark data;  Cluster validity indices;  Emotional factors;  Human actions;  Human behavior;  Human behaviors;  Learning frameworks;  Learning methods;  Q-learning;  Real-world database;  Sequence of actions;  Service systems
Issue Date
201004
Publisher
IEEE COMPUTER SOC
Citation
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, v.22, no.4, pp.479 - 492
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.
URI
http://scholarworks.unist.ac.kr/handle/201301/2947
DOI
http://dx.doi.org/10.1109/TKDE.2009.123
ISSN
1041-4347
Appears in Collections:
ECE_Journal Papers
Files in This Item:
There are no files associated with this item.

find_unist can give you direct access to the published full text of this article. (UNISTARs only)

Show full item record

qr_code

  • mendeley

    citeulike

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

MENU