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Surface EMG and Applications in Gesture Recognition
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Surface EMG and Applications in Gesture Recognition

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (31 July 2021) | Viewed by 7889

Special Issue Editors

Prof. Dr. Athanassios Skodras

E-Mail Website
Guest Editor
University of Patras, Greece
Interests: gesture recognition based on sEMG signals and deep learning; signal, image and video processing/analysis; image and video coding; HDR image compression; data hiding in images and video (watermarking/authentication); fast transform algorithms; digital signal processors and microprocessors; real-time implementation of DSP algorithms
Prof. Dr. Jan Cornelis

E-Mail Website
Guest Editor
Department of Electronics and Informatics (ETRO), Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussel, Belgium
Interests: data analysis; image and video processing; medical imaging; remote sensing; biomedical engineering; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Bart Jansen

E-Mail Website
Guest Editor
Vrije University Brussel, Belgium
Interests: machine learning; serious gaming; deep learning; biomedical signal and image analysis

Special Issue Information

Dear Colleagues,

In recent years the decoding of surface electromyography signals produced by skeletal muscles during the execution of hand gestures has found many applications in human–machine interface, myoelectric control, rehabilitation, and other domains. The availability and diversity of electromyography data has encouraged advancements in processing and data analysis algorithms that pave the way for the development of more complex applications. This also includes fusion with other data types for gesture recognition. This Special Issue aims to communicate novel ideas and new theoretical frameworks/algorithms that are close to real-life applications. It should provide the opportunity for machine learning, robotics, and AI researchers from academia and industry to present their latest work, share ideas, and strengthen contacts within this mixed “EMG/Data Processing” and “University/Industry” R&D community.

Topics of interest include (but are not limited to):

  • Hand gesture recognition from sEMG sensor data;
  • sEMG and other sensors in gesture recognition;
  • Deep learning and gesture recognition;
  • Hand gesture recognition for gaming and virtual reality applications;
  • Hand gesture recognition for robotics applications;
  • Hand gesture recognition for medical and rehabilitation applications;
  • sEMG and IMU sensor large datasets;
  • sEMG in prosthesis control and grasping;
  • sEMG for biometrics applications (person verification and identification).

Prof. Dr. Athanassios Skodras
Prof. Dr. Jan Cornelis
Dr. Bart Jansen
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • surface electromyography
  • machine learning
  • hand gesture recognition
  • classification
  • deep learning
  • time-series analysis and modeling

Published Papers (2 papers)

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Research

17 pages, 1776 KiB  
Article
Real-Time and Offline Evaluation of Myoelectric Pattern Recognition for the Decoding of Hand Movements
by Sara Abbaspour, Autumn Naber, Max Ortiz-Catalan, Hamid GholamHosseini and Maria Lindén
Sensors 2021, 21(16), 5677; https://doi.org/10.3390/s21165677 - 23 Aug 2021
Cited by 7 | Viewed by 3809
Abstract
Pattern recognition algorithms have been widely used to map surface electromyographic signals to target movements as a source for prosthetic control. However, most investigations have been conducted offline by performing the analysis on pre-recorded datasets. While real-time data analysis (i.e., classification when new [...] Read more.
Pattern recognition algorithms have been widely used to map surface electromyographic signals to target movements as a source for prosthetic control. However, most investigations have been conducted offline by performing the analysis on pre-recorded datasets. While real-time data analysis (i.e., classification when new data becomes available, with limits on latency under 200–300 milliseconds) plays an important role in the control of prosthetics, less knowledge has been gained with respect to real-time performance. Recent literature has underscored the differences between offline classification accuracy, the most common performance metric, and the usability of upper limb prostheses. Therefore, a comparative offline and real-time performance analysis between common algorithms had yet to be performed. In this study, we investigated the offline and real-time performance of nine different classification algorithms, decoding ten individual hand and wrist movements. Surface myoelectric signals were recorded from fifteen able-bodied subjects while performing the ten movements. The offline decoding demonstrated that linear discriminant analysis (LDA) and maximum likelihood estimation (MLE) significantly (p < 0.05) outperformed other classifiers, with an average classification accuracy of above 97%. On the other hand, the real-time investigation revealed that, in addition to the LDA and MLE, multilayer perceptron also outperformed the other algorithms and achieved a classification accuracy and completion rate of above 68% and 69%, respectively. Full article
(This article belongs to the Special Issue Surface EMG and Applications in Gesture Recognition)
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16 pages, 14382 KiB  
Article
A Machine Learning Processing Pipeline for Reliable Hand Gesture Classification of FMG Signals with Stochastic Variance
by Mohammed Asfour, Carlo Menon and Xianta Jiang
Sensors 2021, 21(4), 1504; https://doi.org/10.3390/s21041504 - 22 Feb 2021
Cited by 13 | Viewed by 3001
Abstract
ForceMyography (FMG) is an emerging competitor to surface ElectroMyography (sEMG) for hand gesture recognition. Most of the state-of-the-art research in this area explores different machine learning algorithms or feature engineering to improve hand gesture recognition performance. This paper proposes a novel signal processing [...] Read more.
ForceMyography (FMG) is an emerging competitor to surface ElectroMyography (sEMG) for hand gesture recognition. Most of the state-of-the-art research in this area explores different machine learning algorithms or feature engineering to improve hand gesture recognition performance. This paper proposes a novel signal processing pipeline employing a manifold learning method to produce a robust signal representation to boost hand gesture classifiers’ performance. We tested this approach on an FMG dataset collected from nine participants in 3 different data collection sessions with short delays between each. For each participant’s data, the proposed pipeline was applied, and then different classification algorithms were used to evaluate the effect of the pipeline compared to raw FMG signals in hand gesture classification. The results show that incorporating the proposed pipeline reduced variance within the same gesture data and notably maximized variance between different gestures, allowing improved robustness of hand gestures classification performance and consistency across time. On top of that, the pipeline improved the classification accuracy consistently regardless of different classifiers, gaining an average of 5% accuracy improvement. Full article
(This article belongs to the Special Issue Surface EMG and Applications in Gesture Recognition)
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