Research on Cooperative Control of Human-Computer Interaction Tools with High Recognition Rate Based on Neural Network

Human-computer interaction technology simplifies the complicated procedures, which aims at solving the problems of inadequate description and low recognition rate of dance action, studying the action recognition method of dance video image based on human-computer interaction. This method constructs the recognition process based on human-computer interaction technology, constructs the human skeleton model according to the spatial position of skeleton, motion characteristics of skeleton, and change angles of skeleton, describes the dance posture features by generating skeleton node graph, and extracts the key frames of dance video image by using the clustering algorithm to recognize the dance action. The experimental results show that the recognition rate of this method under different entropy values is not less than 88%. Under the test conditions of complex, dark, bright, and multiuser interference, this method can make the model to describe the dance posture accurately. Furthermore, the average recognition rates are 93.43%, 91.27%, 97.15%, and 89.99%, respectively. It is suitable for action recognition of most dance video images.

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Hand Gesture Recognition for Human-Computer Interaction Using Moment Invariants and Neural Network Classifier

Intelligent Robotics and Applications - Lecture Notes in Computer Science ◽

10.1007/978-3-642-40852-6_66 ◽

2013 ◽

pp. 661-667 ◽

Cited By ~ 3

Author(s):

Ran Chen ◽

Shuangyuan Wang ◽

Liang Gong ◽

Chengliang Liu

Keyword(s):

Neural Network ◽

Human Computer Interaction ◽

Gesture Recognition ◽

Hand Gesture Recognition ◽

Hand Gesture ◽

Moment Invariants ◽

Neural Network Classifier ◽

Computer Interaction

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Study on Calibrating Method of FOB Position Tracker Based on Neural Network

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.556-562.5945 ◽

2014 ◽

Vol 556-562 ◽

pp. 5945-5950

Author(s):

Shan Shan Li ◽

Zhong Xiang Zhu ◽

Bo Liu ◽

Zheng He Song ◽

En Rong Mao ◽

...

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Human Computer Interaction ◽

High Accuracy ◽

Bp Algorithm ◽

Calibration Model ◽

Operational Principle ◽

Sampling Points ◽

Computer Interaction

Due to the instability and low precision of electromagnetic position trackers and the inefficiency of existing calibrating methods, a method with high accuracy and effectiveness for FOB (Flock of Birds) calibration was studied. The components, operational principle, merits and drawbacks of FOB were briefly introduced. The positions of 343 sampling points set in the effective working area were measured and the data was processed for trainings and tests of the calibration model established using genetic algorithm and BP algorithm. Experiments were conducted to verify the effectiveness of the method and the results showed the calibrated tracker’s average errors in the X, Y, and Z direction were 0.86cm, 0.70cm and 0.83cm respectively, meeting the requirements of human-computer interaction.

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