AIMC Topic: Electromyography
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Targeted Muscle Reinnervation and Regenerative Peripheral Nerve Interface for Myoelectric Prosthesis Control: The State of Evidence.
Annals of plastic surgery
Jun 1, 2025
Prosthetic rehabilitation after amputation poses significant challenges, often due to functional limitations, residual limb pain (RLP), and phantom limb pain (PLP). These issues not only affect physical health but also mental well-being and quality o...
Neuroadaptive Admittance Control for Human-Robot Interaction With Human Motion Intention Estimation and Output Error Constraint.
IEEE transactions on cybernetics
Jun 1, 2025
Human-robot interaction (HRI) is a crucial component in the field of robotics, and enabling faster response, higher accuracy, as well as smaller human effort, is essential to improve the efficiency, robustness, and applicability of HRI-driven tasks. ...
MVMD-TCCA: A method for gesture classification based on surface electromyographic signals.
Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology
Jun 1, 2025
Gesture recognition plays a fundamental role in enabling nonverbal communication and interaction, as well as assisting individuals with motor impairments in performing daily tasks. Surface electromyographic (sEMG) signals, which can effectively detec...
A multi-label deep residual shrinkage network for high-density surface electromyography decomposition in real-time.
Journal of neuroengineering and rehabilitation
May 8, 2025
BACKGROUND: The swift and accurate identification of motor unit spike trains (MUSTs) from surface electromyography (sEMG) is essential for enabling real-time control in neural interfaces. However, the existing sEMG decomposition methods, including bl...
Real-Time sEMG Processing With Spiking Neural Networks on a Low-Power 5K-LUT FPGA.
IEEE transactions on biomedical circuits and systems
Feb 1, 2025
The accurate modeling of hand movement based on the analysis of surface electromyographic (sEMG) signals offers exciting opportunities for the development of complex prosthetic devices and human-machine interfaces, moving from discrete gesture recogn...
A Soft Robotic Sleeve for Physiotherapy: Improving Elbow Rehabilitation in Baseball Pitchers.
Physiotherapy research international : the journal for researchers and clinicians in physical therapy
Jan 1, 2025
BACKGROUND AND PURPOSE: Throwing a baseball involves intense exposure of the arm to high speeds and powerful forces, which contributes to an increasing prevalence of arm injuries among athletes. Traditional rigid exoskeletons and rehabilitation equip...
[Gesture accuracy recognition based on grayscale image of surface electromyogram signal and multi-view convolutional neural network].
Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
Dec 25, 2024
This study aims to address the limitations in gesture recognition caused by the susceptibility of temporal and frequency domain feature extraction from surface electromyography signals, as well as the low recognition rates of conventional classifiers...
The effectiveness of care robots in alleviating physical burden and pain for caregivers: Non-randomized prospective interventional study - Preliminary study.
Medicine
Dec 13, 2024
BACKGROUND: Caregiver burden significantly affects both patients and caregivers but is often overlooked in clinical practice. Physical and emotional strain on caregivers can compromise the quality of care. Care robots are emerging as solutions to all...
Biosignal-based Control of a Robotic Gait Training Lifter.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2024
In this paper, we present a robotic walker that aims to encourage the patient's voluntary movement by enabling intention-based control of the mobile base. We proposed two variants of biosignal-based control methods for the robotic gait training lifte...
EMGCipher: Decoding Electromyography for Upper-limb Gesture Classification with Explainable AI for Resource Optimization.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2024
Assistive limb devices often employ surface electromyography (sEMG) and deep learning (DL) models for gesture classification. While DL models effectively classify diverse upper-limb gestures, their decision-making mechanisms often lack transparency. ...
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