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
Virtual Robotic Arm Control with Hand Gesture Recognition and Deep Learning Strategies
Karunya Institute of Technology and Sciences, IND.
Karunya University, IND.
Karunya University, IND.
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
2017 (English)In: Advances in Parallel Computing, ISSN 0927-5452, E-ISSN 1879-808X, Vol. 31, p. 50-67Article in journal (Refereed) Published
Abstract [en]

Hand gestures and Deep Learning Strategies can be used to control a virtual robotic arm for real-time applications. A robotic arm which is portable to carry various places and which can be easily programmed to do any work of a hand and is controlled by using deep learning techniques. Deep hand is a combination of both virtual reality and deep learning techniques. It estimated the active spatio-temporal feature and the corresponding pose parameter for various hand movements, to determine the unknown pose parameter of hand gestures by using various deep learning algorithms. A novel framework for hand gestures has been made to estimate by using a deep convolution neural network (CNN) and a deep belief network (DBN). A comparison in terms of accuracy and recognition rate has been drawn. This helps in analyzing the movement of a hand and its fingers which can be made to control a robotic arm with high recognition rate and less error rate. © 2017 The authors and IOS Press. All rights reserved.

Place, publisher, year, edition, pages
IOS Press BV , 2017. Vol. 31, p. 50-67
Keywords [en]
convolution neural network, Deep belief network, Deep learning, hand gesture recognition, kinetic, Restricted Boltzmann Machine, spatio-temporal feature, virtual reality
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:bth-16191DOI: 10.3233/978-1-61499-822-8-50Scopus ID: 2-s2.0-85046353694ISBN: 9781614998211 OAI: oai:DiVA.org:bth-16191DiVA, id: diva2:1206996
Available from: 2018-05-18 Created: 2018-05-18 Last updated: 2018-05-18Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records BETA

Henesey, Lawrence

Search in DiVA

By author/editor
Henesey, Lawrence
By organisation
Department of Computer Science and Engineering
In the same journal
Advances in Parallel Computing
Computer Sciences

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 8 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