methodology-7

Electromyography (EMG)

3260 papers • 126 benchmarks • 313 datasets

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Use these libraries to find electromyography-emg-15 models and implementations

Datasets

Silent Speech EMG

Subtasks

EMG Gesture Recognition ALS Detection EMG Signal Prediction Medial knee JRF Prediction Medial knee JRF Prediction

Most implemented papers

Parkinson’s Disease EMG Data Augmentation and Simulation with DCGANs and Style Transfer

R. A. Zanini, E. Colombini•Thu Apr 30 2020

Two new data augmentation approaches based on Deep Convolutional Generative Adversarial Networks (DCGANs) and Style Transfer for augmenting Parkinson’s Disease electromyography (EMG) signals are proposed.

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Content

Muscle Force Prediction

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sEMG Gesture Recognition with a Simple Model of Attention

David Josephs, Carson Drake, Andrew Heroy, John Santerre•Thu Jun 04 2020

The proposed methodology's model simplicity represents a compelling alternative to the convolutional neural network (CNN) approaches utilized in recent research.

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EV-Action: Electromyography-Vision Multi-Modal Action Dataset

Y. Fu, Bin Sun, Lichen Wang, Joseph P. Robinson, Taotao Jing•Fri Apr 19 2019

This work introduces a new, largescale EV-Action dataset, which consists of RGB, depth, electromyography (EMG), and two skeleton modalities, and introduces an EMG modality which is usually used as an effective indicator in the biomechanics area, but has yet to be well explored in motion related research.

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Parkinson’s Disease EMG Signal Prediction Using Neural Networks

R. A. Zanini, E. Colombini, M. Castro•Mon Sep 30 2019

This paper proposes a comparison between different neural network models, using multilayer perceptron (MLPs) and recurrent neural network (RNN) models, for predicting Parkinson’s disease electromyography (EMG) signals, to anticipate resulting resting tremor patterns. The experimental results indicate that the proposed models can adapt to different frequencies and amplitudes of tremor, and provide reasonable predictions for both EMG envelopes and EMG raw signals. Therefore, one could use these models as input for a control strategy for functional electrical stimulation (FES) devices used on tremor suppression, by dynamically predicting and improving FES control parameters based on tremor forecast.

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Hardware Implementation of Deep Network Accelerators Towards Healthcare and Biomedical Applications

J. Eshraghian, B. Linares‐Barranco, M. Payvand, Elisa Donati, M. Azghadi, C. Lammie, G. Indiveri•Fri Jul 10 2020

A tutorial describing how various technologies including emerging memristive devices, Field Programmable Gate Arrays (FPGAs), and Complementary Metal Oxide Semiconductor (CMOS) can be used to develop efficient DL accelerators to solve a wide variety of diagnostic, pattern recognition, and signal processing problems in healthcare.

174 0

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Digital Voicing of Silent Speech

David Gaddy, Dana Klein•Mon Oct 05 2020

This paper is the first to train speech synthesis models from EMG collected during silently articulated speech by transferring audio targets from vocalized to silent signals, and greatly improves intelligibility of audio generated from silent EMG.

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An Improved Model for Voicing Silent Speech

David Gaddy, Dana Klein•Wed Jun 02 2021

An improved model for voicing silent speech, where audio is synthesized from facial electromyography (EMG) signals, which uses convolutional layers to extract features from the signals and Transformer layers to propagate information across longer distances.

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Towards Predicting Fine Finger Motions from Ultrasound Images via Kinematic Representation

A. Bronstein, Dean Zadok, Oren Salzman, A. Wolf•Wed Feb 09 2022

This work presents an end-to-end robotic system, which can successfully infer fine finger motions, achieved by modeling the hand as a robotic manipulator and using it as an intermediate representation to encode muscles' dynamics from a sequence of US images.

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