Dynamic Gesture Design and Recognition for Human-Robot Collaboration with Convolutional Neural Networks

Abstract

Human-robot collaboration (HRC) is a challenging task in modern industry and gesture communication in HRC has attracted much interest. This paper proposes and demonstrates a dynamic gesture recognition system based on Motion History Image (MHI) and Convolutional Neural Networks (CNN). Firstly, ten dynamic gestures are designed for a human worker to communicate with an industrial robot. Secondly, the MHI method is adopted to extract the gesture features from video clips and generate static images of dynamic gestures as inputs to CNN. Finally, a CNN model is constructed for gesture recognition. The experimental results show very promising classification accuracy using this method.

Meeting Name

2020 International Symposium on Flexible Automation, ISFA 2020 (2020: Jul. 8-9, Virtual)

Department(s)

Mechanical and Aerospace Engineering

Comments

National Science Foundation, Grant NRI-1830479

Keywords and Phrases

Convolutional Neural Networks; Dynamic gesture recognition; Human-robot collaboration; Motion History Image

International Standard Book Number (ISBN)

978-079188361-7

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2020 American Society of Mechanical Engineers (ASME), All rights reserved.

Publication Date

09 Jul 2020

Link to Full Text

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