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Segmenting Humeral Submovements using Invariant Geometric Signatures
KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL. KTH, School of Engineering Sciences (SCI), Centres, BioMEx. Univ Gavle, Dept Elect Math & Nat Sci, Gavle, Sweden..
Univ Gavle, Dept Elect Math & Nat Sci, Gavle, Sweden..
KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL. KTH, School of Engineering Sciences (SCI), Centres, BioMEx.
2017 (English)In: 2017 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS) / [ed] Bicchi, A Okamura, A, IEEE , 2017, p. 6951-6958Conference paper, Published paper (Refereed)
Abstract [en]

Discrete submovements are the building blocks of any complex movement. When robots collaborate with humans, extraction of such submovements can be very helpful in applications such as robot-assisted rehabilitation. Our work aims to segment these submovements based on the invariant geometric information embedded in segment kinematics. Moreover, this segmentation is achieved without any explicit kinematic representation. Our work demonstrates the usefulness of this invariant framework in segmenting a variety of humeral movements, which are performed at different speeds across different subjects. Our results indicate that this invariant framework has high computational reliability despite the inherent variability in human motion.

Place, publisher, year, edition, pages
IEEE , 2017. p. 6951-6958
Series
IEEE International Conference on Intelligent Robots and Systems, ISSN 2153-0858
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-225809ISI: 000426978206070ISBN: 978-1-5386-2682-5 OAI: oai:DiVA.org:kth-225809DiVA, id: diva2:1196041
Conference
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), SEP 24-28, 2017, Vancouver, CANADA
Note

QC 20180409

Available from: 2018-04-09 Created: 2018-04-09 Last updated: 2018-04-09Bibliographically approved

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