Tao-Zhou

sEMG

Codes for srt--hand gesture recognition using sEMG, including download data,data processing,machine learning.

adamyo

Surface EMG-based Inter-session Gesture Recognition Enhanced by Deep Domain Adaptation

arxiv-sanity-preserver

Web interface for browsing, search and filtering recent arxiv submissions

DB1-Ninapro-sEMG-Classification-

DSP_EMGDL_Chapter

Deep Learning approaches for sEMG-based gesture recognition

GAN_generate_ninapro

Using GAN to generate new ninapro data.

MarkdownPhoto

This is my photo album for writing markdown.

NinaproCNN

Convolutional Neural Networks On Ninapro datasets

notes-for-scientific-paper

Some notes for writing paper summarized by myself, and the suggestions from my international friends. Thank them here!

sEMG_DeepLearning

sEMG-based gesture recognition using deep learnig

DeepSLR-Sign-Language-Recognition

基于sEMG和IMU的手语手势识别，包括数据收集、数据预处理（去噪、特征提取，分割）、神经网络搭建、实时识别等。

divide_NinaPro_database_5

This repository contain the code we used to divide NinaPro database 5 into train set and test set

EMG-Movement-Recognition

The source code for the the manuscript titled [M. AbdelMaseeh, T. W. Chen and D. W. Stashuk, "Extraction and Classification of Multichannel Electromyographic Activation Trajectories for Hand Movement Recognition," in IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 24, no. 6, pp. 662-673, June 2016.]. The paper proposes a system for hand movement recognition using multi-channel electromyographic (EMG) signals obtained from the forearm surface. This system can be potentially used to control prostheses or to provide input to a wide range of human computer interface systems. The developed methods were tested with the publicly available NINAPro database.

emg_mc

Machine learning for classifing EMG signals

glow-pytorch-with-gui

This repository contains the gui for playing with the factorized feature learned from the NinaPro database 5.

lihang-code

《统计学习方法》的代码实现

Mymyo_Sunako

Learning about peocessing sEMG image

nina_helper_package

Python functions to aid working with the NinaPro databases (1 & 2)

nina_helper_package_mk2

Python functions and important data for working on NinaPro database 1 & 2

NinaTools

Evaluate gesture detection and recognition models on the NinaPro dataset and newly collected data.

PicGo

sEMG-Neural-Net

Neural network for classifying electromyographic signals into distinct gestures. Additionally, a comparison of CNN vs LSTM implementations.

SemgRecognition

semimyo

Semi-supervised Learning for Surface EMG-based Gesture Recognition

srep

Gesture Recognition by Instantaneous Surface EMG Images