Next Article in Journal
Complete Vision-Based Traffic Sign Recognition Supported by an I2V Communication System
Next Article in Special Issue
Fiber Surface Modification Technology for Fiber-Optic Localized Surface Plasmon Resonance Biosensors
Previous Article in Journal
Sensors Best Paper Award 2012
Previous Article in Special Issue
A Feedfordward Adaptive Controller to Reduce the Imaging Time of Large-Sized Biological Samples with a SPM-Based Multiprobe Station
Article Menu

Export Article

Open AccessArticle
Sensors 2012, 12(2), 1130-1147; doi:10.3390/s120201130

Hand Motion Classification Using a Multi-Channel Surface Electromyography Sensor

Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Hong Kong
Department of Mechanical and Biomedical Engineering, City University of Hong Kong, Hong Kong
Author to whom correspondence should be addressed.
Received: 29 November 2011 / Revised: 4 January 2012 / Accepted: 12 January 2012 / Published: 30 January 2012
(This article belongs to the Special Issue Control Systems and Robotics in Bioengineering)
View Full-Text   |   Download PDF [479 KB, uploaded 21 June 2014]   |  


The human hand has multiple degrees of freedom (DOF) for achieving high-dexterity motions. Identifying and replicating human hand motions are necessary to perform precise and delicate operations in many applications, such as haptic applications. Surface electromyography (sEMG) sensors are a low-cost method for identifying hand motions, in addition to the conventional methods that use data gloves and vision detection. The identification of multiple hand motions is challenging because the error rate typically increases significantly with the addition of more hand motions. Thus, the current study proposes two new methods for feature extraction to solve the problem above. The first method is the extraction of the energy ratio features in the time-domain, which are robust and invariant to motion forces and speeds for the same gesture. The second method is the extraction of the concordance correlation features that describe the relationship between every two channels of the multi-channel sEMG sensor system. The concordance correlation features of a multi-channel sEMG sensor system were shown to provide a vast amount of useful information for identification. Furthermore, a new cascaded-structure classifier is also proposed, in which 11 types of hand gestures can be identified accurately using the newly defined features. Experimental results show that the success rate for the identification of the 11 gestures is significantly high. View Full-Text
Keywords: surface electromyography; multi-channel; hand motion; concordance correlation; cascaded surface electromyography; multi-channel; hand motion; concordance correlation; cascaded

This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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

Tang, X.; Liu, Y.; Lv, C.; Sun, D. Hand Motion Classification Using a Multi-Channel Surface Electromyography Sensor. Sensors 2012, 12, 1130-1147.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics



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