Hand Gesture Recognition Based on Surface Electromyogram Signal (sEMG) with Muscular Contraction Level and Real Time Implementation on An Artificial Prosthetic Wrist Using Artificial Neural Network (ANN)

2020 ◽  
Author(s):  
Ahmed Faiyaz Hasan ◽  
Hozaif Ul Masud ◽  
Tansif Anzar ◽  
Probudha Hasan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Real Time ◽  
Gesture Recognition ◽  
Muscular Contraction ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Surface Electromyogram ◽  
Artificial Neural Network Ann ◽  
Surface Electromyogram Signal

scholarly journals Implementation of Real-Time Static Hand Gesture Recognition Using Artificial Neural Network

2017 ◽  
Vol 11 (2) ◽  
pp. 85 ◽  
Author(s):  
Lita Yusnita ◽  
Rosalina Rosalina ◽  
Rusdianto Roestam ◽  
R. B. Wahyu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Gesture Recognition ◽  
Training Model ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Accuracy Rate ◽  
Question Mark ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

This paper implements static hand gesture recognition in recognizing the alphabetical sign from “A” to “Z”, number from “0” to “9”, and additional punctuation mark such as “Period”, “Question Mark”, and “Space” in Sistem Isyarat Bahasa Indonesia (SIBI). Hand gestures are obtained by evaluating the contourrepresentation from image segmentation of the glove wore by user. Then, it is classified using Artificial Neural Network (ANN) based on the training model previously built from 100 images for each gesture. The accuracy rate of hand gesture translation is calculated to be 90%. Moreover, speech translation recognizes NATO phonetic letter as the speech input for translation.

scholarly journals Real-time Hand-Gesture Recognition based on Deep Neural Network

2021 ◽  
Vol 102 ◽  
pp. 04009
Author(s):  
Naoto Ageishi ◽  
Fukuchi Tomohide ◽  
Abderazek Ben Abdallah
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Gesture Recognition ◽  
Deep Neural Network ◽  
Recognition System ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Robotic Control ◽  
Driver Assistance Systems ◽  
Fast Classification

Hand gestures are a kind of nonverbal communication in which visible bodily actions are used to communicate important messages. Recently, hand gesture recognition has received significant attention from the research community for various applications, including advanced driver assistance systems, prosthetic, and robotic control. Therefore, accurate and fast classification of hand gesture is required. In this research, we created a deep neural network as the first step to develop a real-time camera-only hand gesture recognition system without electroencephalogram (EEG) signals. We present the system software architecture in a fair amount of details. The proposed system was able to recognize hand signs with an accuracy of 97.31%.

scholarly journals Hand Gesture Recognition Using Different Algorithms Based on Artificial Neural Network

2011 ◽  
pp. 128-133
Author(s):  
Shweta K. Yewale ◽  
Pankaj. K. Bharne
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Everyday Life ◽  
Gesture Recognition ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Skin Detection ◽  
Human Beings ◽  
Detection Algorithms ◽  
Machine Interface

Gesture is one of the most natural and expressive ways of communications between human and computer in a real system. We naturally use various gestures to express our own intentions in everyday life. Hand gesture is one of the important methods of non-verbal communication for human beings. Hand gesture recognition based man-machine interface is being developed vigorously in recent years. This paper gives an overview of different methods for recognizing the hand gestures using MATLAB. It also gives the working details of recognition process using Edge detection and Skin detection algorithms.

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