sensors-logo

Journal Browser

Journal Browser

Current Trends and Confounding Factors in Myoelectric Control

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

Deadline for manuscript submissions: closed (31 January 2021) | Viewed by 6415

Special Issue Editors

Institute of Biomedical Engineering, University of New Brunswick, Fredericton, NB, Canada
Interests: pattern recognition; machine learning; signal processing and control; human–machine interfaces; time–frequency analysis; Internet of Things
Special Issues, Collections and Topics in MDPI journals
Institute of Biomedical Engineering, University of New Brunswick, Fredericton, NB, Canada
Interests: EMG signal processing; myoelectric control; pattern recognition; machine learning; gait biomechanics; neuroimaging
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The electromyogram (EMG) signal is an electrophysiological signal that measures currents produced by muscles throughout the human body during contraction, thus representing neuromuscular activity. A common early approach to measuring EMG signals for use as a control input was to place electrodes precisely over specific muscles, now known as sparse multichannel surface EMG. However, to facilitate more general wearable interfaces that could be used in everyday contexts, EMG-based systems must be simple and non-invasive, such as embedded in a socket, a watch, an armband, jewelry, or concealed beneath clothing. Consequently, it is now common to position EMG sensors radially around the circumference of a flexible band. Due to recent advancements and the availability of such EMG sensors, together with advances in wireless communication and embedded computing technologies, EMG data can indeed now be obtained unintrusively using wearable devices. Moreover, impressive advancements have been made in EMG signal processing and pattern recognition over the past few decades. This has greatly increased the number of potential applications for the use of EMG, including, but not limited to, powered prostheses and orthoses, electric power wheelchairs, human–computer interactions, and diagnoses in clinical applications.

Although performance of myoelectric control systems, or EMG pattern recognition, exceeds 90% in controlled environments, myoelectric devices still face challenges in robustness to variability introduced during daily living conditions. Current challenges are commonly associated with this lack of reliability in practical conditions and can be roughly categorized into confounding factors such as limb position, contraction intensity, time (within-day and between-day variability), electrode shift, muscle fatigue, noise, hand-busy conditions, cross-user classification model, etc. New and advanced signal processing and machine learning methods have thus been proposed to minimize the degradation caused by the variation introduced by these aforementioned factors. Robust feature extraction methods, new training strategies, transfer learning and deep learning approaches, and sensor fusion are just some of the emerging and state-of-the-art approaches.

The aim of this Special Issue is to bring together researchers active in the development of EMG sensors, their interpretation, and their applications. Works on innovative EMG signal processing and machine learning algorithms aiming to address critical issues are welcome and encouraged.

Dr. Erik Scheme
Dr. Angkoon Phinyomark
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

  • Electromyography (EMG) signal
  • Myoelectric control
  • Muscle–computer interface
  • Wearable EMG device
  • IMU and sensor fusion
  • EMG feature extraction
  • Dimensionality reduction
  • Classification and gesture recognition
  • Deep learning and transfer learning
  • Limb position and forearm orientation
  • Contraction intensity and muscle force
  • Proportional control
  • Time (within-day and between-day)
  • Electrode shift
  • Muscle fatigue
  • Noise and EMG pre-processing
  • Cross-user classification model

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

44 pages, 3702 KiB  
Article
Current Trends and Confounding Factors in Myoelectric Control: Limb Position and Contraction Intensity
by Evan Campbell, Angkoon Phinyomark and Erik Scheme
Sensors 2020, 20(6), 1613; https://doi.org/10.3390/s20061613 - 13 Mar 2020
Cited by 69 | Viewed by 5851
Abstract
This manuscript presents a hybrid study of a comprehensive review and a systematic (research) analysis. Myoelectric control is the cornerstone of many assistive technologies used in clinical practice, such as prosthetics and orthoses, and human-computer interaction, such as virtual reality control. Although the [...] Read more.
This manuscript presents a hybrid study of a comprehensive review and a systematic (research) analysis. Myoelectric control is the cornerstone of many assistive technologies used in clinical practice, such as prosthetics and orthoses, and human-computer interaction, such as virtual reality control. Although the classification accuracy of such devices exceeds 90% in a controlled laboratory setting, myoelectric devices still face challenges in robustness to variability of daily living conditions. The intrinsic physiological mechanisms limiting practical implementations of myoelectric devices were explored: the limb position effect and the contraction intensity effect. The degradation of electromyography (EMG) pattern recognition in the presence of these factors was demonstrated on six datasets, where classification performance was 13% and 20% lower than the controlled setting for the limb position and contraction intensity effect, respectively. The experimental designs of limb position and contraction intensity literature were surveyed. Current state-of-the-art training strategies and robust algorithms for both effects were compiled and presented. Recommendations for future limb position effect studies include: the collection protocol providing exemplars of at least 6 positions (four limb positions and three forearm orientations), three-dimensional space experimental designs, transfer learning approaches, and multi-modal sensor configurations. Recommendations for future contraction intensity effect studies include: the collection of dynamic contractions, nonlinear complexity features, and proportional control. Full article
(This article belongs to the Special Issue Current Trends and Confounding Factors in Myoelectric Control)
Show Figures

Figure 1

Back to TopTop