Design of a compact low-power human-computer interaction equipment for hand motion

2017 ◽  
Author(s):  
Xianwei Wu ◽  
Wenguang Jin
Keyword(s):  
Human Computer Interaction ◽  
Low Power ◽  
Hand Motion ◽  
Computer Interaction

scholarly journals HUMANOID LOCOMOTION MANIPULATED USING DYNAMIC FINGER GESTURES

2013 ◽  
pp. 170-173
Author(s):  
HARI KRISHNAN R ◽  
VALLIKANNU A. L
Keyword(s):  
Human Computer Interaction ◽  
Degrees Of Freedom ◽  
Motion Tracking ◽  
Human Robot Interaction ◽  
Robot Interaction ◽  
Hand Motion ◽  
Computer Interaction

The fundamental technologies for Human-Computer Interaction are Hand motion tracking and Gesture Identification. The same technology has been adapted for Human-Robot Interaction. This paper discusses a natural methodology for Human-Robot Interaction. In the proposed system, the accelerometers at the fingers, tracks specific gestures. These gestures are identified by the controller, which in turn controls the actuators that results in Humanoid walking. The Humanoid under consideration has 8 Degrees of Freedom.

scholarly journals Hand Motion Analysis using CNN

2020 ◽  
Vol 9 (6) ◽  
pp. 26-30
Keyword(s):  
Neural Network ◽  
Human Computer Interaction ◽  
Motion Analysis ◽  
Network Architecture ◽  
Region Of Interest ◽  
Language Recognition ◽  
Sign Language Recognition ◽  
Hand Motion ◽  
Wide Range ◽  
Computer Interaction

Hand motion detection and gesture recognition research has attracted large interest due to its wide range of applications in the field of Human computer interaction such as sign language recognition, 3D printing, virtual reality. There have been several approaches to create a robust algorithm to ease human computer interaction and perform in unfavourable environments.The real time recognition and learning of the model are big challenges. In this work, we use Convolutional Neural Network architecture to detect and classify hand motions, the region of interest of the image is passed through the neural network for the hand motion analysis and detection.Our system has achieved testing accuracy of 98%.

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