Study of Vision-Based Hand Gesture Recognition System for Astronaut Virtual Training

2014 ◽  
Vol 998-999 ◽  
pp. 1062-1065
Author(s):  
Hong Hu ◽  
Jian Gang Chao ◽  
Zai Qian Zhao
Keyword(s):  
Gesture Recognition ◽  
Recognition System ◽  
Training System ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Recognition Method ◽  
Virtual Training ◽  
Interactive Interface ◽  
Fast Development ◽  
Astronaut Training

With the fast development of vision-based hand gesture recognition, it is possible to apply the technology to astronaut virtual training. In order to solve problems of hand gesture recognition in future virtual training and to provide an unrestricted natural training for astronauts, this paper proposed a vision-based hand gesture recognition method, and implemented a hierarchical gesture recognition system to provide a gesture-driven interactive interface for astronaut virtual training system. The experiment results showed that this recognition system can be used to help astronaut training.

Feature Fusion Based Hand Gesture Recognition Method for Automotive Interfaces

2020 ◽  
Vol 29 (6) ◽  
pp. 1153-1164
Author(s):  
Qianyi Xu ◽  
Guihe Qin ◽  
Minghui Sun ◽  
Jie Yan ◽  
Huiming Jiang ◽  
...  
Keyword(s):  
Gesture Recognition ◽  
Feature Fusion ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Recognition Method

scholarly journals Surface EMG-Based Instantaneous Hand Gesture Recognition Using Convolutional Neural Network with the Transfer Learning Method

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2540
Author(s):  
Zhipeng Yu ◽  
Jianghai Zhao ◽  
Yucheng Wang ◽  
Linglong He ◽  
Shaonan Wang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Gesture Recognition ◽  
Recognition System ◽  
Surface Emg ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Training Time ◽  
Generalization Ability

In recent years, surface electromyography (sEMG)-based human–computer interaction has been developed to improve the quality of life for people. Gesture recognition based on the instantaneous values of sEMG has the advantages of accurate prediction and low latency. However, the low generalization ability of the hand gesture recognition method limits its application to new subjects and new hand gestures, and brings a heavy training burden. For this reason, based on a convolutional neural network, a transfer learning (TL) strategy for instantaneous gesture recognition is proposed to improve the generalization performance of the target network. CapgMyo and NinaPro DB1 are used to evaluate the validity of our proposed strategy. Compared with the non-transfer learning (non-TL) strategy, our proposed strategy improves the average accuracy of new subject and new gesture recognition by 18.7% and 8.74%, respectively, when up to three repeated gestures are employed. The TL strategy reduces the training time by a factor of three. Experiments verify the transferability of spatial features and the validity of the proposed strategy in improving the recognition accuracy of new subjects and new gestures, and reducing the training burden. The proposed TL strategy provides an effective way of improving the generalization ability of the gesture recognition system.

scholarly journals FPGA based Real Time Human Hand Gesture Recognition System

2012 ◽  
Vol 6 ◽  
pp. 98-107 ◽  
Author(s):  
Amit Gupta ◽  
Vijay Kumar Sehrawat ◽  
Mamta Khosla
Keyword(s):  
Real Time ◽  
Gesture Recognition ◽  
Recognition System ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Human Hand

scholarly journals AN EMG BASED HAND GESTURE RECOGNITION SYSTEM USING SVM

2019 ◽  
Vol 6 (6) ◽  
pp. 40-44
Author(s):  
Vijayalakshmi G V ◽  
Ajay J ◽  
Pavithra S ◽  
Pooja Eronisha A ◽  
Vanijayam K
Keyword(s):  
Gesture Recognition ◽  
Recognition System ◽  
Hand Gesture Recognition ◽  
Hand Gesture
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