Next Article in Journal
Unmanned Aerial Vehicle Based Wireless Sensor Network for Marine-Coastal Environment Monitoring
Previous Article in Journal
A Novel Technique for Fetal ECG Extraction Using Single-Channel Abdominal Recording
Article Menu
Issue 3 (March) cover image

Export Article

Open AccessArticle
Sensors 2017, 17(3), 458;

Surface EMG-Based Inter-Session Gesture Recognition Enhanced by Deep Domain Adaptation

State Key Lab of CAD&CG, College of Computer Science, Zhejiang University, Hangzhou 310027, China
College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China
Author to whom correspondence should be addressed.
Academic Editor: Vittorio M. N. Passaro
Received: 29 December 2016 / Revised: 3 February 2017 / Accepted: 21 February 2017 / Published: 24 February 2017
(This article belongs to the Section Physical Sensors)
Full-Text   |   PDF [1120 KB, uploaded 24 February 2017]   |  


High-density surface electromyography (HD-sEMG) is to record muscles’ electrical activity from a restricted area of the skin by using two dimensional arrays of closely spaced electrodes. This technique allows the analysis and modelling of sEMG signals in both the temporal and spatial domains, leading to new possibilities for studying next-generation muscle-computer interfaces (MCIs). sEMG-based gesture recognition has usually been investigated in an intra-session scenario, and the absence of a standard benchmark database limits the use of HD-sEMG in real-world MCI. To address these problems, we present a benchmark database of HD-sEMG recordings of hand gestures performed by 23 participants, based on an 8 × 16 electrode array, and propose a deep-learning-based domain adaptation framework to enhance sEMG-based inter-session gesture recognition. Experiments on NinaPro, CSL-HDEMG and our CapgMyo dataset validate that our approach outperforms state-of-the-arts methods on intra-session and effectively improved inter-session gesture recognition. View Full-Text
Keywords: muscle-computer interface; electromyography; gesture recognition; domain adaptation muscle-computer interface; electromyography; gesture recognition; domain adaptation

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).

Share & Cite This Article

MDPI and ACS Style

Du, Y.; Jin, W.; Wei, W.; Hu, Y.; Geng, W. Surface EMG-Based Inter-Session Gesture Recognition Enhanced by Deep Domain Adaptation. Sensors 2017, 17, 458.

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



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