Hand gesture recognition in natural human-computer interaction

Research Article
Open access

Hand gesture recognition in natural human-computer interaction

Chang Ge 1* , Jianhua Min 2
  • 1 Columbia International College    
  • 2 American Straight Academy    
  • *corresponding author 2023081047FBK@cic.care
Published on 7 February 2024 | https://doi.org/10.54254/2755-2721/36/20230430
ACE Vol.36
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-297-8
ISBN (Online): 978-1-83558-298-5

Abstract

This paper introduces the definition of Gesture recognition. First the article gives a precise definition of gesture recognition and explains the difference between gestures and postures, then it reveals technical difficulties of Gesture recognition. These technical difficulties include four aspects. After analyzing the technology and methods of Gesture recognition and the technical difficulties, it concretely expounds gesture recognition process based on data gloves, which is widely studied before and it also introduces the computer vision based research achievement which is currently becoming a research hotspot in this field. Then it also takes Kinect and HoloLens as examples to introduce specific practical cases of gesture recognition in wearable devices. Also it outlines the application of gesture recognition technology in human-computer interaction which include but not limited to smart terminal, Game control, Robot Control, Clinical and Health, Smart home, Sign Language Recognition, Vehicle system, Interactive entertainment. Finally it reach the conclusion that the biggest challenge researchers meet is to build a powerful framework to overcome the common problems with fewer constraints to provide reliable results. And sometimes researchers need to combine multiple methods for different complex environments.

Keywords:

Human–computer Interaction, Gesture Recognition, Data Glove, Computer Vision

Ge,C.;Min,J. (2024). Hand gesture recognition in natural human-computer interaction. Applied and Computational Engineering,36,111-118.
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References

[1]. Guo K, Zhang S, Zhao S, et al 2021. Design and manufacture of data gloves for rehabilitation training and gesture recognition based on flexible sensors. Journal of Healthcare Engineering, Access 13.

[2]. Li Y, Yang L, He Z, et al 2022. Low‐cost data glove based on deep‐learning‐enhanced flexible multiwalled carbon nanotube sensors for real‐time gesture recognition Advanced Intelligent Systems, 4(11): 2200128.

[3]. Oudah M, Al-Naji A, Chahl J 2020. Hand gesture recognition based on computer vision: a review of techniques. journal of Imaging, 6(8), 73.

[4]. He Maolin. (2016). Research and Implementation of Gesture Recognition Algorithms Based on Computer Vision. (Doctoral dissertation, University of Electronic Science and Technology of China).

[5]. Luo Guoqiang, Li Jiahua, Zuo Wentao, Fang Bin. Research on Steps and Methods of Gesture Recognition Based on Computer Vision Technology Wireless Internet Technology Magazine Agency 2020, Vol. 17 Issue (3): 148-149.doi: 10.0002/1672-6944-1718

[6]. Khan R Z, Ibraheem N A 2012. Hand gesture recognition: a literature review. International journal of artificial Intelligence & Applications, 3(4), 161.

[7]. Li Wensheng, Xie Mei, Deng Chunjian. A dynamic multi-point gesture recognition method based on machine vision [J]. Computer Engineering and Design, 2012 (5): 1988-1992.

[8]. Tang M 2011. Recognizing hand gestures with microsoft’s kinect. Palo Alto: Department of Electrical Engineering of Stanford University: [sn], 23.

[9]. Pal D H, Kakade S M 2016. Dynamic hand gesture recognition using kinect sensor. In 2016 International Conference on Global Trends in Signal Processing, Information Computing and Communication (ICGTSPICC) 448-453.

[10]. Murthy G R S, Jadon R S 2009 . A review of vision based hand gestures recognition. International Journal of Information Technology and Knowledge Management 2(2): 405-410.

Cite this article

Ge,C.;Min,J. (2024). Hand gesture recognition in natural human-computer interaction. Applied and Computational Engineering,36,111-118.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 2023 International Conference on Machine Learning and Automation

ISBN：978-1-83558-297-8(Print) / 978-1-83558-298-5(Online)
Editor：Mustafa İSTANBULLU
Conference website: https://2023.confmla.org/
Conference date: 18 October 2023
Series: Applied and Computational Engineering
Volume number: Vol.36
ISSN：2755-2721(Print) / 2755-273X(Online)

© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. Authors who publish this series agree to the following terms:
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References

[1]. Guo K, Zhang S, Zhao S, et al 2021. Design and manufacture of data gloves for rehabilitation training and gesture recognition based on flexible sensors. Journal of Healthcare Engineering, Access 13.

[2]. Li Y, Yang L, He Z, et al 2022. Low‐cost data glove based on deep‐learning‐enhanced flexible multiwalled carbon nanotube sensors for real‐time gesture recognition Advanced Intelligent Systems, 4(11): 2200128.

[3]. Oudah M, Al-Naji A, Chahl J 2020. Hand gesture recognition based on computer vision: a review of techniques. journal of Imaging, 6(8), 73.

[4]. He Maolin. (2016). Research and Implementation of Gesture Recognition Algorithms Based on Computer Vision. (Doctoral dissertation, University of Electronic Science and Technology of China).

[5]. Luo Guoqiang, Li Jiahua, Zuo Wentao, Fang Bin. Research on Steps and Methods of Gesture Recognition Based on Computer Vision Technology Wireless Internet Technology Magazine Agency 2020, Vol. 17 Issue (3): 148-149.doi: 10.0002/1672-6944-1718

[6]. Khan R Z, Ibraheem N A 2012. Hand gesture recognition: a literature review. International journal of artificial Intelligence & Applications, 3(4), 161.

[7]. Li Wensheng, Xie Mei, Deng Chunjian. A dynamic multi-point gesture recognition method based on machine vision [J]. Computer Engineering and Design, 2012 (5): 1988-1992.

[8]. Tang M 2011. Recognizing hand gestures with microsoft’s kinect. Palo Alto: Department of Electrical Engineering of Stanford University: [sn], 23.

[9]. Pal D H, Kakade S M 2016. Dynamic hand gesture recognition using kinect sensor. In 2016 International Conference on Global Trends in Signal Processing, Information Computing and Communication (ICGTSPICC) 448-453.

[10]. Murthy G R S, Jadon R S 2009 . A review of vision based hand gestures recognition. International Journal of Information Technology and Knowledge Management 2(2): 405-410.

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