Next Article in Journal
Development of a Low-Cost Arduino-Based Sonde for Coastal Applications
Previous Article in Journal
An Artificial Intelligence Approach for Gears Diagnostics in AUVs
Article Menu

Export Article

Open AccessArticle
Sensors 2016, 16(4), 530; doi:10.3390/s16040530

MGRA: Motion Gesture Recognition via Accelerometer

1
Department of Computer Science and Technology, Ocean University of China, Qingdao 266100, China
2
School of Computer Science and Engineering, The University of New South Wales, Sydney 2052, Australia
*
Author to whom correspondence should be addressed.
Academic Editor: Angelo Maria Sabatini
Received: 15 February 2016 / Revised: 30 March 2016 / Accepted: 5 April 2016 / Published: 13 April 2016
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [1172 KB, uploaded 13 April 2016]   |  

Abstract

Accelerometers have been widely embedded in most current mobile devices, enabling easy and intuitive operations. This paper proposes a Motion Gesture Recognition system (MGRA) based on accelerometer data only, which is entirely implemented on mobile devices and can provide users with real-time interactions. A robust and unique feature set is enumerated through the time domain, the frequency domain and singular value decomposition analysis using our motion gesture set containing 11,110 traces. The best feature vector for classification is selected, taking both static and mobile scenarios into consideration. MGRA exploits support vector machine as the classifier with the best feature vector. Evaluations confirm that MGRA can accommodate a broad set of gesture variations within each class, including execution time, amplitude and non-gestural movement. Extensive evaluations confirm that MGRA achieves higher accuracy under both static and mobile scenarios and costs less computation time and energy on an LG Nexus 5 than previous methods. View Full-Text
Keywords: accelerometer; gesture recognition; SVM; feature selection accelerometer; gesture recognition; SVM; feature selection
Figures

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

Hong, F.; You, S.; Wei, M.; Zhang, Y.; Guo, Z. MGRA: Motion Gesture Recognition via Accelerometer. Sensors 2016, 16, 530.

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]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top