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 7842
Special Issue Editors
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
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
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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
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