scholarly journals The Virtual Trackpad: An Electromyography-Based, Wireless, Real-Time, Low-Power, Embedded Hand-Gesture-Recognition System Using an Event-Driven Artificial Neural Network

2017 ◽  
Vol 64 (11) ◽  
pp. 1257-1261 ◽  
Author(s):  
Xilin Liu ◽  
Jacob Sacks ◽  
Milin Zhang ◽  
Andrew G. Richardson ◽  
Timothy H. Lucas ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Low Power ◽  
Real Time ◽  
Gesture Recognition ◽  
Recognition System ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Event Driven ◽  
Artificial Neural

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%.

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