A Dynamic Gesture Recognition System to Translate between Sign Languages in Complex Backgrounds

2016 ◽  
Author(s):  
Davi Hirafuji Neiva ◽  
Cleber Zanchettin
Keyword(s):  
Gesture Recognition ◽  
Recognition System ◽  
Sign Languages ◽  
Dynamic Gesture Recognition

scholarly journals A Generic Multi-modal Dynamic Gesture Recognition System Using Machine Learning

2018 ◽  
pp. 603-615 ◽  
Author(s):  
G. Gautham Krishna ◽  
Karthik Subramanian Nathan ◽  
B. Yogesh Kumar ◽  
Ankith A. Prabhu ◽  
Ajay Kannan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Gesture Recognition ◽  
Recognition System ◽  
Dynamic Gesture Recognition

Methods on Real Time Gesture Recognition System

2018 ◽  
Vol 7 (3.12) ◽  
pp. 982
Author(s):  
Anuradha Patil ◽  
Chandrashekhar M. Tavade ◽  
. .
Keyword(s):  
Gesture Recognition ◽  
High Efficiency ◽  
Recognition System ◽  
Medical Diagnostics ◽  
Hand Position ◽  
Sign Languages ◽  
Smoothing Methods ◽  
Image Capturing ◽  
Gesture Segmentation ◽  
Gesture Tracking

Gesture recognition deals with discussion of various methods, techniques and concerned algorithms related to it. Gesture recognition uses a simple & basic sign languages like movement of hand, position of lips & eye ball as well as eye lids positions. The various methods for image capturing, gesture recognition, gesture tracking, gesture segmentation and smoothing methods compared, and by the overweighing advantage of different gesture recognitions and their applications.  In recent days gesture recognition is widely utilized in gaming industries, biomedical applications, and medical diagnostics for dumb and deaf people. Due to their wide applications, high efficiency, high accuracy and low expenditure gestures are using in many applications including robotics. By using gestures to develop human computer interaction (HCI) method it is necessary to identify the proper and meaning full gesture from different gesture images. The Gesture recognition avoids use of costly hardware devices for understanding the activities and recognition example lots of I/O devices like keyboard mouse etc. Can be Limited.  

A Dynamic Gesture Recognition System based on CIPBR Algorithm

Polibits ◽  
2014 ◽  
Vol 50 ◽  
pp. 13-19 ◽  
Author(s):  
Diego G.S. Santos ◽  
Rodrigo C. Neto ◽  
Bruno J.T. Fernandes ◽  
Byron L.D. Bezerra
Keyword(s):  
Gesture Recognition ◽  
Recognition System ◽  
Dynamic Gesture Recognition

Dynamic Gesture Design and Recognition for Human-Robot Collaboration With Convolutional Neural Networks

2020 ◽  
Author(s):  
Haodong Chen ◽  
Wenjin Tao ◽  
Ming C. Leu ◽  
Zhaozheng Yin
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Gesture Recognition ◽  
Industrial Robot ◽  
Recognition System ◽  
Dynamic Gestures ◽  
Static Images ◽  
Gesture Design ◽  
Dynamic Gesture Recognition ◽  
Human Robot Collaboration

Abstract Human-robot collaboration (HRC) is a challenging task in modern industry and gesture communication in HRC has attracted much interest. This paper proposes and demonstrates a dynamic gesture recognition system based on Motion History Image (MHI) and Convolutional Neural Networks (CNN). Firstly, ten dynamic gestures are designed for a human worker to communicate with an industrial robot. Secondly, the MHI method is adopted to extract the gesture features from video clips and generate static images of dynamic gestures as inputs to CNN. Finally, a CNN model is constructed for gesture recognition. The experimental results show very promising classification accuracy using this method.

scholarly journals Development of a Verbal Robot Hand Gesture Recognition System

2021 ◽  
Vol 16 ◽  
pp. 573-583
Author(s):  
Chingis Kenshimov ◽  
Talgat Sundetov ◽  
Murat Kunelbayev ◽  
Zhazira Amirgaliyeva ◽  
Didar Yedilkhan ◽  
...  
Keyword(s):  
Gesture Recognition ◽  
Recognition System ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Accurate Information ◽  
Robot Hand ◽  
Sign Languages ◽  
Human Voice ◽  
Touch Sensor ◽  
Communication Module

This article analyzes the most famous sign languages, the correlation of sign languages, and also considers the development of a verbal robot hand gesture recognition system in relation to the Kazakh language. The proposed system contains a touch sensor, in which the contact of the electrical property of the user's skin is measured, which provides more accurate information for simulating and indicating the gestures of the robot hand. Within the framework of the system, the speed and accuracy of recognition of each gesture of the verbal robot are calculated. The average recognition accuracy was over 98%. The detection time was 3ms on a 1.9 GHz Jetson Nano processor, which is enough to create a robot showing natural language gestures. A complete fingerprint of the Kazakh sign language for a verbal robot is also proposed. To improve the quality of gesture recognition, a machine learning method was used. The operability of the developed technique for recognizing gestures by a verbal robot was tested, and on the basis of computational experiments, the effectiveness of algorithms and software for responding to a verbal robot to a voice command was evaluated based on automatic recognition of a multilingual human voice. Thus, we can assume that the authors have proposed an intelligent verbal complex implemented in Python with the CMUSphinx communication module and the PyOpenGL graphical command execution simulator. Robot manipulation module based on 3D modeling from ABB.

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