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Layered HMM for motion intention recognition
KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS. KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS. KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.ORCID iD: 0000-0003-2965-2953
2006 (English)In: 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, Vols 1-12, NEW YORK: IEEE , 2006, 5130-5135 p.Conference paper, Published paper (Refereed)
Abstract [en]

Acquiring, representing and modeling human skins is one of the key research areas in teleoperation, programming. by-demonstration and human-machine collaborative settings. One of the common approaches is to divide the task that the operator is executing into several subtasks in order to provide manageable modeling. In this paper we consider the use of a Layered Hidden Markov Model (LHMM) to model human skills. We evaluate a gestem classifier that classifies motions into basic action-primitives, or gestems. The gestem classifiers are then used in a LHMM to model a simulated teleoperated task. We investigate the online and offline classilication performance with respect to noise, number of gestems, type of HAIM and the available number of training sequences. We also apply the LHMM to data recorded during the execution of a trajectory-tracking task in 2D and 3D with a robotic manipulator in order to give qualitative as well as quantitative results for the proposed approach. The results indicate that the LHMM is suitable for modeling teleoperative trajectory-tracking tasks and that the difference in classification performance between one and multi dimensional HMMs for gestem classification is small. It can also be seen that the LHMM is robust w.r.t misclassifications in the underlying gestem classifiers.

Place, publisher, year, edition, pages
NEW YORK: IEEE , 2006. 5130-5135 p.
National Category
Robotics
Identifiers
URN: urn:nbn:se:kth:diva-42066DOI: 10.1109/IROS.2006.282606ISI: 000245452405034Scopus ID: 2-s2.0-34250652173ISBN: 978-1-4244-0258-8 (print)OAI: oai:DiVA.org:kth-42066DiVA: diva2:445878
Conference
IEEE/RSJ International Conference on Intelligent Robots and Systems. Beijing, PEOPLES R CHINA. OCT 09-13, 2006
Note
QC 20111005Available from: 2011-10-05 Created: 2011-10-05 Last updated: 2012-01-24Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
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