Hand Movement Identification Using Single-Stream Spatial Convolutional Neural Networks

2020 ◽  
Author(s):  
Aldi Sidik Permana ◽  
Esmeralda Contessa Djamal ◽  
Fikri Nugraha ◽  
Fatan Kasyidi
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Hand Movement ◽  
Single Stream

scholarly journals Prediction of Hand Movement Speed and Force from Single-trial EEG with Convolutional Neural Networks

2018 ◽  
Author(s):  
Ramiro Gatti ◽  
Yanina Atum ◽  
Luciano Schiaffino ◽  
Mads Jochumsen ◽  
José Biurrun Manresa
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Hand Movement ◽  
Substantial Improvement ◽  
Movement Speed ◽  
Single Trial ◽  
Eeg Signals ◽  
Brain Signals ◽  
Electroencephalogram Eeg

AbstractBuilding accurate movement decoding models from brain signals is crucial for many biomedical applications. Decoding specific movement features, such as speed and force, may provide additional useful information at the expense of increasing the complexity of the decoding problem. Recent attempts to predict movement speed and force from the electroencephalogram (EEG) achieved classification accuracy levels not better than chance, stressing the demand for more accurate prediction strategies. Thus, the aim of this study was to improve the prediction accuracy of hand movement speed and force from single-trial EEG signals recorded from healthy volunteers. A strategy based on convolutional neural networks (ConvNets) was tested, since it has previously shown good performance in the classification of EEG signals. ConvNets achieved an overall accuracy of 84% in the classification of two different levels of speed and force (4-class classification) from single-trial EEG. These results represent a substantial improvement over previously reported results, suggesting that hand movement speed and force can be accurately predicted from single-trial EEG.

scholarly journals CURSOR MANIPULATION WITH HAND RECOGNITION USING COMPUTER VISION

2021 ◽  
Vol 9 (1) ◽  
pp. 1455-1456
Author(s):  
Mandar Salvi, Shravan Kegade, Aniket Shinde, Bhanu Tekwani
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Convolutional Neural Networks ◽  
Hand Movement ◽  
Computing System ◽  
Video Frame ◽  
Software Program ◽  
Hand Recognition

This paper aims to make a software program which will Track/Monitor your hand movement in front of the screen through a webcam and will move the cursor of the computing system with respect to your hand movement and can do certain fixed tasks like Right Click, Left Click, Scroll, Drag, Switch Between Programs, Go back, Forward, etc. This program will work in background and use convolutional Neural Networks Model (SSD) to convolve each and every video frame coming from input and at the end will classify the image into classes after further processing of the predicted class it will do necessary operations on Mouse/ Trackpad driver to perform desired operations.

CNN-based Classification of Degraded Images

2020 ◽  
Vol 2020 (10) ◽  
pp. 28-1-28-7 ◽  
Author(s):  
Kazuki Endo ◽  
Masayuki Tanaka ◽  
Masatoshi Okutomi
Keyword(s):  
Neural Networks ◽  
Image Restoration ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
Alternative Approach ◽  
Degraded Image ◽  
Degraded Images ◽  
Straightforward Approach

Classification of degraded images is very important in practice because images are usually degraded by compression, noise, blurring, etc. Nevertheless, most of the research in image classification only focuses on clean images without any degradation. Some papers have already proposed deep convolutional neural networks composed of an image restoration network and a classification network to classify degraded images. This paper proposes an alternative approach in which we use a degraded image and an additional degradation parameter for classification. The proposed classification network has two inputs which are the degraded image and the degradation parameter. The estimation network of degradation parameters is also incorporated if degradation parameters of degraded images are unknown. The experimental results showed that the proposed method outperforms a straightforward approach where the classification network is trained with degraded images only.

Convolutional Neural Networks for Underwater Pipeline Segmentation using Imperfect Datasets

2021 ◽  
Author(s):  
Edgar Medina ◽  
Roberto Campos ◽  
Jose Gabriel R. C. Gomes ◽  
Mariane R. Petraglia ◽  
Antonio Petraglia
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Underwater Pipeline
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