scholarly journals Intelligent Human-Computer Interaction Based on Surface EMG Gesture Recognition

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 61378-61387 ◽  
Author(s):  
Jinxian Qi ◽  
Guozhang Jiang ◽  
Gongfa Li ◽  
Ying Sun ◽  
Bo Tao
Keyword(s):  
Human Computer Interaction ◽  
Gesture Recognition ◽  
Surface Emg ◽  
Computer Interaction

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 To Control Electrical MCBs using Hand Gesture Recognition

2015 ◽  
Vol 14 (9) ◽  
pp. 6102-6106
Author(s):  
Sangeeta Goyal ◽  
Dr. Bhupesh Kumar
Keyword(s):  
Human Computer Interaction ◽  
Gesture Recognition ◽  
Human Body ◽  
Circuit Breaker ◽  
Hand Gesture Recognition ◽  
Hand Movements ◽  
Hand Gesture ◽  
Handicapped Person ◽  
New Techniques ◽  
Computer Interaction

There has been growing interest in development of new techniques and methods for Human-Computer Interaction (HCI). Gesture Recognition is one of the important areas of this technology. Gesture Recognition means interfacing with computer using motion of human body typically hand movements. As a Handicapped person cannot move very easily and quickly if there is a fire in house or he/she cannot switch off the Miniature Circuit Breaker (MCB) but the same task can be done easily with hand gesture recognition. In our proposed system electrical MCB can be controlled using hand gesture recognizer. To switch on/off the MCB, we need to provide hand based gesture as an input to system.

Sixth Sense Technology

2016 ◽  
pp. 188-215 ◽  
Author(s):  
Zeenat S. AlKassim ◽  
Nader Mohamed
Keyword(s):  
Artificial Intelligence ◽  
Image Processing ◽  
Computer Vision ◽  
Augmented Reality ◽  
Human Computer Interaction ◽  
Gesture Recognition ◽  
Real World ◽  
Digital World ◽  
The Future ◽  
Computer Interaction

In this chapter, the authors discuss a unique technology known as the Sixth Sense Technology, highlighting the future opportunities of such technology in integrating the digital world with the real world. Challenges in implementing such technologies are also discussed along with a review of the different possible implementation approaches. This review is performed by exploring the different inventions in areas similar to the Sixth Sense Technology, namely augmented reality (AR), computer vision, image processing, gesture recognition, and artificial intelligence and then categorizing and comparing between them. Lastly, recommendations are discussed for improving such a unique technology that has the potential to create a new trend in human-computer interaction (HCI) in the coming years.

scholarly journals Posture and Gesture Recognition for Human-Computer Interaction

10.5772/8221 ◽  
2009 ◽  
Author(s):  
Mahmoud Elmezain ◽  
Ayoub Al-Hamadi ◽  
Omer Rashid ◽  
Bernd Michaelis
Keyword(s):  
Human Computer Interaction ◽  
Gesture Recognition ◽  
Computer Interaction
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