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Electromyography

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A Non-Intrusive Neural Quality Assessment Model for Surface Electromyography Signals.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
In practical scenarios involving the measurement of surface electromyography (sEMG) in muscles, particularly those areas near the heart, one of the primary sources of contamination is the presence of electrocardiogram (ECG) signals. To assess the qua...

Deep Generative Replay-based Class-incremental Continual Learning in sEMG-based Pattern Recognition.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Developments in neural networks and sensing technologies have increased focus on modules for surface electromyogram (sEMG)-based pattern recognition. Incremental updating of parameters based on pre-trained networks can flexibly respond to user requir...

A Graph Neural Network Model for Real-Time Gesture Recognition Based on sEMG Signals.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
For seemless control of advanced hand prostheses and augmented reality, accurate and immediate hand gestures recognition is essential. Surface electromyography (sEMG) signals obtained from the forearm are commonly employed for this purpose. In this p...

FedAssist: Federated Learning in AI-Powered Prosthetics for Sustainable and Collaborative Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
This paper explores the integration of federated learning in developing deep learning-powered surface electromyography decoding methods for AI-controlled prosthetics. Our proposed FL framework, FedAssist, aims to preserve data ownership while fosteri...

EMG gesture signal analysis towards diagnosis of upper limb using dual-pathway convolutional neural network.

Mathematical biosciences and engineering : MBE
This research introduces a novel dual-pathway convolutional neural network (DP-CNN) architecture tailored for robust performance in Log-Mel spectrogram image analysis derived from raw multichannel electromyography signals. The primary objective is to...

[Research on mode adjustment control strategy of upper limb rehabilitation robot based on fuzzy recognition of interaction force].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
In the process of robot-assisted training for upper limb rehabilitation, a passive training strategy is usually used for stroke patients with flaccid paralysis. In order to stimulate the patient's active rehabilitation willingness, the rehabilitation...

Deep Learning-based Thigh Muscle Investigation Using MRI For Prosthetic Development for Patients Undergoing Total Knee Replacement (TKR).

Current medical imaging
BACKGROUND: A prosthetic device is designed based on the quantitative analysis of muscle MRI which will improve the muscle control achieved with functional electrical stimulation/ guided robotic exoskeletons. Electromyography (EMG) provides muscle fu...

Hybrid Rehabilitation System with Motion Estimation Based on EMG Signals.

IEEE ... International Conference on Rehabilitation Robotics : [proceedings]
Patients with upper limb paralysis undergo various types of rehabilitation to reconstruct upper limb functions necessary for their return to daily life and social activities. Therefore, it is necessary to develop an effective rehabilitation support s...

1D-Convolutional Neural Networks can Quantify Therapy Content of Children and Adolescents Walking in a Robot-Assisted Gait Trainer.

IEEE ... International Conference on Rehabilitation Robotics : [proceedings]
Therapy content, consisting of device parameter settings and therapy instructions, is crucial for an effective robot-assisted gait therapy program. Settings and instructions depend on the therapy goals of the individual patient. While device paramete...

EMG-Based Control Strategies of a Supernumerary Robotic Hand for the Rehabilitation of Sub-Acute Stroke Patients: Proof of Concept.

IEEE ... International Conference on Rehabilitation Robotics : [proceedings]
One of the most frequent and severe aftermaths of a stroke is the loss of upper limb functionality. Therapy started in the sub-acute phase proved more effective, mainly when the patient participates actively. Recently, a novel set of rehabilitation a...