AIMC Topic: Electromyography

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The effect of the 2-UPS/RR ankle rehabilitation robot with coupling biomechanical model on muscle behaviors.

Medical & biological engineering & computing
With the popularization of biomechanical simulation technology, aiming at the rehabilitation of ankle joint injury, we imported simplified model of proposed 2-UPS/RR (two identical unconstraint kinematic branches with a universal-prismatic-spherical ...

EMG-driven shared human-robot compliant control for in-hand object manipulation in hand prostheses.

Journal of neural engineering
. The limited functionality of hand prostheses remains one of the main reasons behind the lack of its wide adoption by amputees. Indeed, while commercial prostheses can perform a reasonable number of grasps, they are often inadequate for manipulating...

Continuous Estimation of Human Joint Angles From sEMG Using a Multi-Feature Temporal Convolutional Attention-Based Network.

IEEE journal of biomedical and health informatics
Intention recognition based on surface electromyography (sEMG) signals is pivotal in human-machine interaction (HMI), where continuous motion estimation with high accuracy has been the challenge. The convolutional neural network (CNN) possesses excel...

Fuzzy inference system (FIS) - long short-term memory (LSTM) network for electromyography (EMG) signal analysis.

Biomedical physics & engineering express
A wide range of application domains,s such as remote robotic control, rehabilitation, and remote surgery, require capturing neuromuscular activities. The reliability of the application is highly dependent on an ability to decode intentions accurately...

MSFF-Net: Multi-Stream Feature Fusion Network for surface electromyography gesture recognition.

PloS one
In the field of surface electromyography (sEMG) gesture recognition, how to improve recognition accuracy has been a research hotspot. The rapid development of deep learning provides a new solution to this problem. At present, the main applications of...

A Novel Application of Deep Learning (Convolutional Neural Network) for Traumatic Spinal Cord Injury Classification Using Automatically Learned Features of EMG Signal.

Sensors (Basel, Switzerland)
In this study, a traumatic spinal cord injury (TSCI) classification system is proposed using a convolutional neural network (CNN) technique with automatically learned features from electromyography (EMG) signals for a non-human primate (NHP) model. A...

Urologic latency time during uroflow stop test with electromyography: an incontinence detector in rehabilitation after robotic radical prostatectomy.

European journal of physical and rehabilitation medicine
BACKGROUND: Stress urinary incontinence (UI) is the most common presentation following robot-assisted radical prostatectomy (RARP), but a postoperative non-invasive and objective test is still lacking. To assess pelvic floor integrity after RARP, we ...

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
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...

sEMG-Based Gain-Tuned Compliance Control for the Lower Limb Rehabilitation Robot during Passive Training.

Sensors (Basel, Switzerland)
The lower limb rehabilitation robot is a typical man-machine coupling system. Aiming at the problems of insufficient physiological information and unsatisfactory safety performance in the compliance control strategy for the lower limb rehabilitation ...

Ultrathin crystalline-silicon-based strain gauges with deep learning algorithms for silent speech interfaces.

Nature communications
A wearable silent speech interface (SSI) is a promising platform that enables verbal communication without vocalization. The most widely studied methodology for SSI focuses on surface electromyography (sEMG). However, sEMG suffers from low scalabilit...