Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
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.ORCID iD: 0000-0002-5970-2985
KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL. KTH, School of Engineering Sciences (SCI), Centres, BioMEx.ORCID iD: 0000-0003-2078-8854
2017 (English)In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 6951-6958, article id 8206619Conference 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
Institute of Electrical and Electronics Engineers (IEEE), 2017. p. 6951-6958, article id 8206619
Series
IEEE International Conference on Intelligent Robots and Systems, ISSN 2153-0858
National Category
Robotics
Identifiers
URN: urn:nbn:se:kth:diva-224352DOI: 10.1109/IROS.2017.8206619Scopus ID: 2-s2.0-85041961221ISBN: 9781538626825 OAI: oai:DiVA.org:kth-224352DiVA, id: diva2:1191491
Conference
2017 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2017, Vancouver, Canada, 24 September 2017 through 28 September 2017
Note

QC 20180319

Available from: 2018-03-19 Created: 2018-03-19 Last updated: 2018-03-19Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records BETA

Rakesh, KrishnanSmith, Christian

Search in DiVA

By author/editor
Rakesh, KrishnanSmith, Christian
By organisation
Robotics, perception and learning, RPLBioMEx
Robotics

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 3 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf