scholarly journals Research and Application of Multifeature Gesture Recognition in Human-Computer Interaction Based on Virtual Reality Technology

2021 ◽  
Vol 2021 ◽  
pp. 1-5
Author(s):  
Zhao Feng ◽  
Jinlong Wu ◽  
Taile Ni
Keyword(s):  
Virtual Reality ◽  
Human Computer Interaction ◽  
Gesture Recognition ◽  
Central Region ◽  
Feature Recognition ◽  
Feature Fusion ◽  
Time Dynamic ◽  
Information Recognition ◽  
Dynamic Gesture Recognition ◽  
Computer Interaction

Objective. To explore the research and application of multifeature gesture recognition in virtual reality human-computer interaction and to explore the gesture recognition technology scheme to achieve better human-computer interaction experience. Methods. Through the study of the technical difficulties of gesture recognition, comparative static gesture feature recognition and feature fusion algorithms are applied, in the process of research on gesture partition, and adjust the contrast of characteristic parameters, combined with the feature of space-time dynamic gesture tracking trajectory and dynamic gesture recognition and gesture recognition effect under different scheme. Results. The central region was divided into 0 regions, and the central region was divided into 1-4 regions in counterclockwise direction. Compared with the traditional gesture changes, the overlapping problem in the four partition modes was reduced, the gesture was better displayed, and the operation and use of gesture processing were realized more efficiently. Conclusion. Gesture recognition requires the combination of static gesture feature information recognition, gesture feature fusion, spatiotemporal trajectory feature, and dynamic gesture trajectory feature to achieve a better human-computer interaction experience.

scholarly journals Dynamic Gesture Recognition Using Surface EMG Signals Based on Multi-Stream Residual Network

2021 ◽  
Vol 9 ◽  
Author(s):  
Zhiwen Yang ◽  
Du Jiang ◽  
Ying Sun ◽  
Bo Tao ◽  
Xiliang Tong ◽  
...  
Keyword(s):  
Human Computer Interaction ◽  
Gesture Recognition ◽  
Hand Movement ◽  
Surface Emg ◽  
Residual Network ◽  
Semg Signal ◽  
Essential Information ◽  
Emg Signal ◽  
Dynamic Gesture Recognition ◽  
Computer Interaction

Gesture recognition technology is widely used in the flexible and precise control of manipulators in the assisted medical field. Our MResLSTM algorithm can effectively perform dynamic gesture recognition. The result of surface EMG signal decoding is applied to the controller, which can improve the fluency of artificial hand control. Much current gesture recognition research using sEMG has focused on static gestures. In addition, the accuracy of recognition depends on the extraction and selection of features. However, Static gesture research cannot meet the requirements of natural human-computer interaction and dexterous control of manipulators. Therefore, a multi-stream residual network (MResLSTM) is proposed for dynamic hand movement recognition. This study aims to improve the accuracy and stability of dynamic gesture recognition. Simultaneously, it can also advance the research on the smooth control of the Manipulator. We combine the residual model and the convolutional short-term memory model into a unified framework. The architecture extracts spatiotemporal features from two aspects: global and deep, and combines feature fusion to retain essential information. The strategy of pointwise group convolution and channel shuffle is used to reduce the number of network calculations. A dataset is constructed containing six dynamic gestures for model training. The experimental results show that on the same recognition model, the gesture recognition effect of fusion of sEMG signal and acceleration signal is better than that of only using sEMG signal. The proposed approach obtains competitive performance on our dataset with the recognition accuracies of 93.52%, achieving state-of-the-art performance with 89.65% precision on the Ninapro DB1 dataset. Our bionic calculation method is applied to the controller, which can realize the continuity of human-computer interaction and the flexibility of manipulator control.

scholarly journals A Novel Hand Gesture Recognition Based on High-Level Features

2018 ◽  
Vol 15 (02) ◽  
pp. 1750022 ◽  
Author(s):  
Jing Li ◽  
Jianxin Wang ◽  
Zhaojie Ju
Keyword(s):  
Human Computer Interaction ◽  
Real Time ◽  
Gesture Recognition ◽  
Feature Fusion ◽  
Recognition System ◽  
Depth Information ◽  
Static Gesture Recognition ◽  
Time Requirements ◽  
High Level ◽  
Computer Interaction

Gesture recognition plays an important role in human–computer interaction. However, most existing methods are complex and time-consuming, which limit the use of gesture recognition in real-time environments. In this paper, we propose a static gesture recognition system that combines depth information and skeleton data to classify gestures. Through feature fusion, hand digit gestures of 0–9 can be recognized accurately and efficiently. According to the experimental results, the proposed gesture recognition system is effective and robust, which is invariant to complex background, illumination changes, reversal, structural distortion, rotation, etc. We have tested the system both online and offline which proved that our system is satisfactory to real-time requirements, and therefore it can be applied to gesture recognition in real-world human–computer interaction systems.

scholarly journals Gesture Detection and Recognition Based on Pyramid Frequency Feature Fusion Module and Multiscale Attention in Human-Computer Interaction

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Min Tu
Keyword(s):  
Human Computer Interaction ◽  
Gesture Recognition ◽  
Feature Fusion ◽  
Low Frequency ◽  
Extraction Process ◽  
Detection Accuracy ◽  
Computational Overhead ◽  
Frequency Feature ◽  
Detection And Recognition ◽  
Computer Interaction

Aiming at the problem of the absence of detail texture and other high-frequency features in the feature extraction process of the deep network employing the upsampling operation, the accuracy of gesture recognition is seriously affected in complex scenes. This study integrates object detection and gesture recognition into one model and proposes a gesture detection and recognition based on the pyramid frequency feature fusion module and multiscale attention in human-computer interaction. Pyramid fusion module is used to perform efficient feature fusion and is proposed to obtain feature layers with rich details and semantic information, which is helpful to improve the efficiency and accuracy of gesture recognition. In addition, the multiscale attention module is further adopted to adaptively mine important and effective feature information from both temporal and spatial channels and embedded into the detection layer. Finally, our proposed network realizes the enhancement of the effective information and the suppression of the invalid information of the detection layer. Experimental results show that our proposed model makes full use of the high-low frequency feature fusion module without replacing the basic backbone network, which can greatly reduce the computational overhead while improving the detection accuracy.

“There is Nothing New in Cyberspace” a Motion for Debate

1992 ◽  
Vol 36 (14) ◽  
pp. 1049-1049 ◽  
Author(s):  
Maxwell J. Wells
Keyword(s):  
Artificial Intelligence ◽  
Information Technology ◽  
Virtual Reality ◽  
Human Computer Interaction ◽  
Cold Fusion ◽  
Major Advance ◽  
Junk Bonds ◽  
Exciting Development ◽  
Computer Interaction ◽  
The Way

Cyberspace is the environment created during the experience of virtual reality. Therefore, to assert that there is nothing new in cyberspace alludes to there being nothing new about virtual reality. Is this assertion correct? Is virtual reality an exciting development in human-computer interaction, or is it simply another example of effective simulation? Does current media interest herald a major advance in information technology, or will virtual reality go the way of artificial intelligence, cold fusion and junk bonds? Is virtual reality the best thing since sliced bread, or is it last week's buns in a new wrapper?

A 28 nm, 397 μW real-time dynamic gesture recognition chip based on RISC-V processor

2021 ◽  
pp. 105219
Author(s):  
Yong-Liang Zhang ◽  
Qiang Li ◽  
Hui Zhang ◽  
Wei-Zhen Wang ◽  
Jun Han ◽  
...  
Keyword(s):  
Real Time ◽  
Gesture Recognition ◽  
Time Dynamic ◽  
Dynamic Gesture Recognition ◽  
28 Nm
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