AIMC Topic: Isometric Contraction
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Tailoring neuromuscular dynamics: A modeling framework for realistic sEMG simulation.
PloS one
Jun 12, 2025
This study introduces an advanced computational model for simulating surface electromyography (sEMG) signals during muscle contractions. The model integrates five elements that simulate the chain of processes from motor intention to voltage variation...
A Novel Approach to Detecting Muscle Fatigue Based on sEMG by Using Neural Architecture Search Framework.
IEEE transactions on neural networks and learning systems
Aug 4, 2023
Muscle fatigue detection is of great significance to human physiological activities, but many complex factors increase the difficulty of this task. In this article, we integrate several effective techniques to distinguish muscle states under fatigue ...
Toward a generalizable deep CNN for neural drive estimation across muscles and participants.
Journal of neural engineering
Jan 18, 2023
High-density electromyography (HD-EMG) decomposition algorithms are used to identify individual motor unit (MU) spike trains, which collectively constitute the neural code of movements, to predict motor intent. This approach has advanced from offline...
A Deep CNN Framework for Neural Drive Estimation From HD-EMG Across Contraction Intensities and Joint Angles.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Oct 28, 2022
OBJECTIVE: Previous studies have demonstrated promising results in estimating the neural drive to muscles, the net output of all motoneurons that innervate the muscle, using high-density electromyography (HD-EMG) for the purpose of interfacing with a...
A computer vision approach for classifying isometric grip force exertion levels.
Ergonomics
Apr 10, 2020
Exposure to high and/or repetitive force exertions can lead to musculoskeletal injuries. However, measuring worker force exertion levels is challenging, and existing techniques can be intrusive, interfere with human-machine interface, and/or limited ...
Identification of the best strategy to command variable stiffness using electromyographic signals.
Journal of neural engineering
Feb 18, 2020
OBJECTIVE: In the last decades, many EMG-controlled robotic devices were developed. Since stiffness control may be required to perform skillful interactions, different groups developed devices whose stiffness is real-time controlled based on EMG sign...
Effects of mechanical assistance on muscle activity and motor performance during isometric elbow flexion.
Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology
Nov 30, 2019
Mechanical assistance on joint movement is generally beneficial; however, its effects on cooperative performance and muscle activity needs to be further explored. This study examined how motor performance and muscle activity are altered when mechanic...
Muscle endurance time estimation during isometric training using electromyogram and supervised learning.
Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology
Nov 16, 2019
UNLABELLED: Constant-force isometric muscle training is useful for increasing the maximal strength , rehabilitation and work-fatigue assessment. Earlier studies have shown that muscle fatigue characteristics can be used for evaluating muscle enduranc...
Physiological and kinematic effects of a soft exosuit on arm movements.
Journal of neuroengineering and rehabilitation
Feb 22, 2019
BACKGROUND: Soft wearable robots (exosuits), being lightweight, ergonomic and low power-demanding, are attractive for a variety of applications, ranging from strength augmentation in industrial scenarios, to medical assistance for people with motor i...
Feasibility Study of Advanced Neural Networks Applied to sEMG-Based Force Estimation.
Sensors (Basel, Switzerland)
Sep 25, 2018
To find out the feasibility of different neural networks in sEMG-based force estimation, in this paper, three types of networks, namely convolutional neural network (CNN), long short-term memory (LSTM) network and their combination (C-LSTM) were appl...
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