NDLI: Understanding a child's play for robot interaction by sequencing play primitives using Hidden Markov Models

Content Provider	IEEE Xplore Digital Library
Author	Hae Won Park Howard, A.M.
Copyright Year	2010
Description	Author affiliation: School of Electrical and Computer Engineering at Georgia Institute of Technology, Atlanta, USA (Hae Won Park; Howard, A.M.)
Abstract	In this paper, we discuss a methodology to build a system for a robot playmate that extracts and sequences low-level play primitives during a robot-child interaction scenario. The motivation is to provide a robot with basic knowledge of how to manipulate toys in an equivalent manner as a human does - as a first step in engaging children in cooperative play. Our approach involves the extraction of play primitives based on observation of motion gradient vectors computed from the image sequence. Hidden Markov Models (HMMs) are then used to recognize 14 different play primitives during play. Experimental results from a data set of 100 play scenarios including child subjects demonstrate 86.88% accuracy recognizing and sequencing the play primitives.
Starting Page	170
Ending Page	177
File Size	1214415
Page Count	8
File Format	PDF
ISBN	9781424450381
ISSN	10504729
DOI	10.1109/ROBOT.2010.5509156
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-05-03
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hidden Markov models Pediatrics Pattern recognition Robotics and automation Intelligent robots Data mining Image sequences USA Councils Humans Social factors
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Control and Systems Engineering Electrical and Electronic Engineering Software

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