Next Article in Journal
Mode Coupling Properties of the Plasmonic Dimers Composed of Graphene Nanodisks
Next Article in Special Issue
A New Framework of Human Interaction Recognition Based on Multiple Stage Probability Fusion
Previous Article in Journal
Self-Organized Nanoscale Roughness Engineering for Broadband Light Trapping in Thin Film Solar Cells
Previous Article in Special Issue
Tangible User Interface and Mu Rhythm Suppression: The Effect of User Interface on the Brain Activity in Its Operator and Observer
Article Menu
Issue 4 (April) cover image

Export Article

Open AccessArticle
Appl. Sci. 2017, 7(4), 358; doi:10.3390/app7040358

Multiple Sensors Based Hand Motion Recognition Using Adaptive Directed Acyclic Graph

1
School of Automation, Wuhan University of Technology, Wuhan 430070, China
2
School of Computing, The University of Portsmouth, Portsmouth PO1 3HE, UK
3
School of Electrical and Mechanical Engineering, Pingdingshan University, Pingdingshan 467000, China
*
Author to whom correspondence should be addressed.
Academic Editors: Plamen Angelov and José Antonio Iglesias Martínez
Received: 19 February 2017 / Revised: 28 March 2017 / Accepted: 30 March 2017 / Published: 5 April 2017
(This article belongs to the Special Issue Human Activity Recognition)
View Full-Text   |   Download PDF [1826 KB, uploaded 19 April 2017]   |  

Abstract

The use of human hand motions as an effective way to interact with computers/robots, robot manipulation learning and prosthetic hand control is being researched in-depth. This paper proposes a novel and effective multiple sensor based hand motion capture and recognition system. Ten common predefined object grasp and manipulation tasks demonstrated by different subjects are recorded from both the human hand and object points of view. Three types of sensors, including electromyography, data glove and FingerTPS are applied to simultaneously capture the EMG signals, the finger angle trajectories, and the contact force. Recognising different grasp and manipulation tasks based on the combined signals is investigated by using an adaptive directed acyclic graph algorithm, and results of comparative experiments show the proposed system with a higher recognition rate compared with individual sensing technology, as well as other algorithms. The proposed framework contains abundant information from multimodal human hand motions with the multiple sensor techniques, and it is potentially applicable to applications in prosthetic hand control and artificial systems performing autonomous dexterous manipulation. View Full-Text
Keywords: EMG; contact force; data glove; adaptive directed acyclic graph EMG; contact force; data glove; adaptive directed acyclic graph
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Xue, Y.; Ju, Z.; Xiang, K.; Chen, J.; Liu, H. Multiple Sensors Based Hand Motion Recognition Using Adaptive Directed Acyclic Graph. Appl. Sci. 2017, 7, 358.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Appl. Sci. EISSN 2076-3417 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top