Name: Alex-Zhou
Type: User
Company: University of Jinan (UJN)
Bio: MS in Computer Science and Technology - Medical Robot, Signal Processing, Deep Learning, Medical Image Analysis.
Now is visiting student in SIAT.
Location: Shenzhen, Guangdong, China
Blog: http://uslab.ujn.edu.cn/
Alex-Zhou's Projects
Surface EMG-based Inter-session Gesture Recognition Enhanced by Deep Domain Adaptation
Web interface for browsing, search and filtering recent arxiv submissions
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。
This repository contain the code we used to divide NinaPro database 5 into train set and test set
Deep Learning approaches for sEMG-based gesture 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.
Machine learning for classifing EMG signals
Using GAN to generate new ninapro data.
This repository contains the gui for playing with the factorized feature learned from the NinaPro database 5.
《统计学习方法》的代码实现
This is my photo album for writing markdown.
Learning about peocessing sEMG image
Python functions to aid working with the NinaPro databases (1 & 2)
Python functions and important data for working on NinaPro database 1 & 2
Convolutional Neural Networks On Ninapro datasets
Evaluate gesture detection and recognition models on the NinaPro dataset and newly collected data.
Some notes for writing paper summarized by myself, and the suggestions from my international friends. Thank them here!
Codes for srt--hand gesture recognition using sEMG, including download data,data processing,machine learning.
Neural network for classifying electromyographic signals into distinct gestures. Additionally, a comparison of CNN vs LSTM implementations.
sEMG-based gesture recognition using deep learnig
Semi-supervised Learning for Surface EMG-based Gesture Recognition
Gesture Recognition by Instantaneous Surface EMG Images