Online, interactive learning of gestures for human/robot interfaces

Author

Lee, Christopher ; Xu, Yangsheng

Author_Institution

Robotics Inst., Carnegie Mellon Univ., Pittsburgh, PA, USA

Volume

4

fYear

1996

fDate

22-28 Apr 1996

Firstpage

2982

Abstract

We have developed a gesture recognition system, based on hidden Markov models, which can interactively recognize gestures and perform online learning of new gestures. In addition, it is able to update its model of a gesture iteratively with each example it recognizes. This system has demonstrated reliable recognition of 14 different gestures after only one or two examples of each. The system is currently interfaced to a Cyberglove for use in recognition of gestures from the sign language alphabet. The system is being implemented as part of an interactive interface for robot teleoperation and programming by example

Keywords

hidden Markov models; intelligent control; interactive systems; iterative methods; learning systems; man-machine systems; pattern classification; real-time systems; robot programming; telerobotics; Cyberglove; gesture recognition system; hidden Markov models; human/robot interfaces; interactive interface; iterative method; online interactive learning; sign language alphabet; teleoperation; Data gloves; Education; Educational robots; Face recognition; Handicapped aids; Hidden Markov models; Human robot interaction; Keyboards; Robot programming; Robotics and automation;

fLanguage

English

Publisher

ieee

Conference_Titel

Robotics and Automation, 1996. Proceedings., 1996 IEEE International Conference on

Conference_Location

Minneapolis, MN

ISSN

1050-4729

Print_ISBN

0-7803-2988-0

Type

conf

DOI

10.1109/ROBOT.1996.509165

Filename

509165