NDLI: Analysis of Human Motion for Humanoid Robots

Content Provider	IEEE Xplore Digital Library
Author	Moldenhauer, J. Boesnach, I. Beth, T. Wank, V. Bos, K.
Copyright Year	2005
Description	Author affiliation: Institute for Algorithms and Cognitive Systems University of Karlsruhe Am Fasanengarten 5, 76131 Karlsruhe, Germany; jomo@ira.uka.de (Moldenhauer, J.)
Abstract	A great challange in robotics is to make robots more like humans. One important aspect is to make robots move like humans and recognize their motions. Both tasks are based on human motion trajectories and require a proper modelling. To accomplish these tasks, we acquire data from complex motions like setting the table, pouring water into a cup, or stirring the content of the cup. The objectives of our studies are to identify the subject doing the motion and to detect slight changes in motion constraints with automatic classification methods. For that purpose, we present reliable methods based on Elman networks and hidden Markov models. We develop the model parameters and compare the performance of the methods, especially, the classification results and the suitability for the classification tasks.
Starting Page	311
Ending Page	316
File Size	458819
Page Count	6
File Format	PDF
ISBN	078038914X
DOI	10.1109/ROBOT.2005.1570137
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-04-18
Publisher Place	Spain
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Humans Motion analysis Humanoid robots Hidden Markov models Cognitive robotics Robotics and automation Data acquisition Testing Tracking Magnetic sensors hidden Markov models Human motion classification Elman networks
Content Type	Text
Resource Type	Article

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