AIMC Topic: Electromyography
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Predicting lower body joint moments and electromyography signals using ground reaction forces during walking and running: An artificial neural network approach.
Gait & posture
Jan 21, 2025
BACKGROUND: This study leverages Artificial Neural Networks (ANNs) to predict lower limb joint moments and electromyography (EMG) signals from Ground Reaction Forces (GRF), providing a novel perspective on human gait analysis. This approach aims to e...
Surmounting the ceiling effect of motor expertise by novel sensory experience with a hand exoskeleton.
Science robotics
Jan 15, 2025
For trained individuals such as athletes and musicians, learning often plateaus after extensive training, known as the "ceiling effect." One bottleneck to overcome it is having no prior physical experience with the skill to be learned. Here, we chall...
Design and Validation of a Pancake Style Planetary Gearbox for an Eddy Current-Based Wearable Gait Training Robot.
IEEE transactions on bio-medical engineering
Jan 15, 2025
Eddy current brakes have been recently used for functional resistance training in individuals with neurological and orthopaedic disorders. These devices consist of a gearbox, a conductive disc, and permanent magnets that can be moved relative to the ...
TCNN-KAN: Optimized CNN by Kolmogorov-Arnold Network and Pruning Techniques for sEMG Gesture Recognition.
IEEE journal of biomedical and health informatics
Jan 7, 2025
Surface electromyography (sEMG) is a non-invasive technique that records the electrical signals generated by muscle activity. sEMG signals are widely used in the field of biomedical and health informatics for diagnosing and monitoring neuromuscular d...
Deep learning enhanced transmembranous electromyography in the diagnosis of sleep apnea.
BMC neuroscience
Dec 31, 2024
Obstructive sleep apnea (OSA) is widespread, under-recognized, and under-treated, impacting the health and quality of life for millions. The current gold standard for sleep apnea testing is based on the in-lab sleep study, which is costly, cumbersome...
Two-dimensional identification of lower limb gait features based on the variational modal decomposition of sEMG signal and convolutional neural network.
Gait & posture
Dec 28, 2024
BACKGROUND: Gait feature recognition is crucial to improve the efficiency and coordination of exoskeleton assistance. The recognition methods based on surface electromyographic (sEMG) signals are popular. However, the recognition accuracy of these me...
Highly Responsive Robotic Prosthetic Hand Control Considering Electrodynamic Delay.
Sensors (Basel, Switzerland)
Dec 27, 2024
As robots become increasingly integrated into human society, the importance of human-machine interfaces continues to grow. This study proposes a faster and more accurate control system for myoelectric prostheses by considering the Electromechanical D...
Multimodal data-based human motion intention prediction using adaptive hybrid deep learning network for movement challenged person.
Scientific reports
Dec 24, 2024
Recently, social demands for a good quality of life have increased among the elderly and disabled people. So, biomedical engineers and robotic researchers aimed to fuse these techniques in a novel rehabilitation system. Moreover, these models utilize...
Augmented Effect of Combined Robotic Assisted Gait Training and Proprioceptive Neuromuscular Facilitation-irradiation Technique on Muscle Activation and Ankle Kinematics in Hemiparetic Gait: A Preliminary Study.
NeuroRehabilitation
Dec 19, 2024
BackgroundProprioceptive neuromuscular facilitation (PNF) alone has limited effectiveness in restoring gait, while robotic-assisted gait training (RAGT) improves motor relearning through repetitive, task-specific movements. Combining PNF with robotic...
A U-Net based partial convolutional time-domain separation model to identify motor units from surface electromyographic signals in real time.
Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology
Dec 19, 2024
This study proposed a U-Net based partial convolutional time-domain model for a real-time high-density surface electromyography (HD-sEMG) decomposition. The model combines U-Net and a separation block containing partial convolution, aiming to efficie...
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