Proceedings of the 3rd Annual International Conference on Mechanics and Mechanical Engineering (MME 2016)

Inference of User's Intention for Human - Robot Cooperation Based on Machine Vision

Authors
Bei-Bei Yang, Ge Liu, Xiao-Fan He, Di Zhao
Corresponding Author
Bei-Bei Yang
Available Online December 2016.
DOI
https://doi.org/10.2991/mme-16.2017.102How to use a DOI?
Keywords
Natural human-computer interaction, Intention prediction, Weight distribution, Dominant factor, Machine vision.
Abstract
An intention prediction algorithm for natural human-computer interaction based on machine vision is proposed in this paper. Firstly, the motion data of human skeletal feature point is acquired. Then, the motion data and the real-time interactive image are coupled through data processing. Meanwhile, an intention recognition model for natural human-computer interaction is built based on target feature extraction. The dominant feature weight of the operator intention is distributed by hierarchical method. A parallel scheme is adopted in this algorithm for operator intention recognition. Through experiment and data analysis, the algorithm is proved to be reliable and instrumental for improving the efficiency of natural human-computer interaction.
Open Access
This is an open access article distributed under the CC BY-NC license.

Download article (PDF)

Proceedings
3rd Annual International Conference on Mechanics and Mechanical Engineering (MME 2016)
Part of series
Advances in Engineering Research
Publication Date
December 2016
ISBN
978-94-6252-303-6
ISSN
2352-5401
DOI
https://doi.org/10.2991/mme-16.2017.102How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Bei-Bei Yang
AU  - Ge Liu
AU  - Xiao-Fan He
AU  - Di Zhao
PY  - 2016/12
DA  - 2016/12
TI  - Inference of User's Intention for Human - Robot Cooperation Based on Machine Vision
BT  - 3rd Annual International Conference on Mechanics and Mechanical Engineering (MME 2016)
PB  - Atlantis Press
SN  - 2352-5401
UR  - https://doi.org/10.2991/mme-16.2017.102
DO  - https://doi.org/10.2991/mme-16.2017.102
ID  - Yang2016/12
ER  -