Hand gesture recognition using multimodal data fusion and multiscale parallel convolutional neural network for human–robot interaction-Reference-Cited by-同舟云学术

Hand gesture recognition using multimodal data fusion and multiscale parallel convolutional neural network for human–robot interaction

Published:2020-01-07 Issue:5 Volume:38 Page:
ISSN:0266-4720
Container-title:Expert Systems
language:en
Short-container-title:Expert Systems

Author:

Gao Qing¹²^ORCID,Liu Jinguo¹^ORCID,Ju Zhaojie¹³^ORCID

Affiliation:

1. State Key Laboratory of Robotics, Shenyang Institute of Automation, Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China

2. University of Chinese Academy of Sciences Beijing China

3. School of Computing University of Portsmouth Portsmouth UK

Funder

National Natural Science Foundation of China

Publisher

Wiley

Subject

Artificial Intelligence,Computational Theory and Mathematics,Theoretical Computer Science,Control and Systems Engineering

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/exsy.12490

Reference24 articles.

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2. Boyali A. Hashimoto N. &Matsumoto O.(2015).Hand posture and gesture recognition using MYO armband and spectral collaborative representation based classification. In2015 IEEE 4th Global Conference on Consumer Electronics (GCCE) IEEE pp.200–201.

3. Chen C. Jafari R. &Kehtarnavaz N.(2015).Utd‐mhad: A multimodal dataset for human action recognition utilizing a depth camera and a wearable inertial sensor. In2015 IEEE International conference on image processing (ICIP) IEEE pp.168–172.

4. Multi‐biometric systems: A state of the art survey and research directions;EL‐SAYED A.;IJACSA) International Journal of Advanced Computer Science and Applications,2015

5. Gao Q. Liu J. Ju Z. Li Y. Zhang T. &Zhang L.(2017a).Static hand gesture recognition with parallel CNNs for space human‐robot interaction. InInternational Conference on Intelligent Robotics and Applications Springer pp.462–473.

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