research-article

Multi-modal hand gesture designing in multi-screen touchable teaching system for human-computer interaction

Authors:

Yingying ChenAuthors Info & Claims

ICAIP '18: Proceedings of the 2nd International Conference on Advances in Image Processing

Pages 198 - 202

https://doi.org/10.1145/3239576.3239619

Published: 16 June 2018 Publication History

Abstract

In this paper, we developed a hand gesture recognition technology in a human-computer interaction system with several hand gestures. The proposed system can help teacher to control the multi-screen touchable teaching tools, such as sweeping right or left to access the previous or next slide, with a fist to call the eraser tool to rub out the wrong content. To verify its efficiency and other qualities, we conducted a quasi-experiment in our program site in east part of China which analyzed the pre- and post-test scores in Math class of each experimental groups. Moreover, the error recognition rate is reduced by increasing the relevant features and threshold training for the teaching application. Experimental results demonstrate that the proposed system achieves the high accuracy and real-time performance.

References

[1]

J.S. Bridle, 1989. Probabilistic Interpretation of Feedforward Classification Network Outputs, with Relationships to Statistical Pattern Recognition, Neurocomputing-Algorithms, Architectures and Applications, F. Fogelman-Soulie and J. Herault, eds., NATO ASI Series F68, Berlin: Springer-Verlag, pp. 227--236.

[2]

C. Li, X. Zhang, and L. Jin. LPSNet: 2017. A Novel Log Path Signature Feature Based Hand Gesture Recognition Framework, in IEEE International Conference on Computer Vision Workshops (ICCVW), 631--639.

[3]

S. Sridhar, A. Oulasvirta, and C. Theobalt. 2013. Interactive Markerless Articulated Hand Motion Tracking Using RGB and Depth Data, in IEEE International Conference on Computer Vision (ICCV), pp. 2456--2463,

Digital Library

[4]

Y. Wang, J. Min, and J. Zhang, 2013. Video-based hand manipulation capture through composite motion control, ACM Trans. Graph., 32, 1--14.

Digital Library

[5]

C. J. Kaufman. 1992. Rocky Mountain Research Laboratories, Boulder, Colo., personal communication.

[6]

C. Li, X. Zhang, and L. Jin. 2017. LPSNet: A Novel Log Path Signature Feature Based Hand Gesture Recognition Framework, in 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), 631--639.

[7]

T. Liu, Z. Chen, and A. M. Lesgold, 2017. Novelty Blended Learning Pattern and Its Application in English Language Teaching. at the Proceedings of the International Conference on Digital Technology in Education, Taipei, Taiwan.

Digital Library

[8]

D. M. Gavrila. 1999. The visual analysis of human movement: A survey. Computer Vision and Image Understanding, 73:82--98.

Digital Library

[9]

H. Goto, Y. Hasegawa, and M. Tanaka. 2007. Efficient Scheduling Focusing on the Duality of MPL Representation, Proc. IEEE Symp. Computational Intelligence in Scheduling (SCIS '07), pp. 57--64,

[10]

F. Mueller, "Real-Time Hand Tracking under Occlusion from an Egocentric RGB-D Sensor," in IEEE International Conference on Computer Vision (ICCV), 2017, 1163--1172.

[11]

E.E. Reber, "Oxygen Absorption in the Earth's Atmosphere," Technical Report TR-0200 (420-46)-3, Aerospace Corp., Los Angeles, Calif., Nov. 1988.

[12]

T. Liu, 2016. Blind image restoration with sparse priori regularization for passive millimeter-wave images. Journal of Visual Communication and Image Representation, 40, pp. 58--66.

Digital Library

[13]

T. Liu, and H. Liu, 2018. FBRDLR: Fast blind reconstruction approach with dictionary learning regularization for infrared microscopy spectra. Infrared Physics & Technology, 90, 101--109.

[14]

T. Liu, H. Liu, and Z. Chen, 2018. Fast Blind Instrument Function Estimation Method for Industrial Infrared Spectrometers. IEEE Transactions on Industrial Informatics,

[15]

H. Liu, and Y. Chen, 2017. Cloud-Terminal Integration Learning Platform and Its Applications in Blended Learning. in International Symposium on Educational Technology (ISET), Hong Kong, pp. 71--73.

[16]

L. Hubert and P. Arabie, 1985. Comparing Partitions," J. Classification, 2, 4, 193--218.

[17]

H. Liu, L. Yan, and Y. Chang, 2013. Spectral deconvolution and feature extraction with robust adaptive Tikhonov regularization. IEEE Trans. on Instrumentation and Measurement, 62, 315--327.

[18]

H. Liu. 2017. Blind Spectral Signal Deconvolution with Sparsity Regularization: An Iteratively Reweighted Least-Squares Solution. Circuits, Systems, and Signal Processing, 36, 435--446.

Digital Library

[19]

H. Liu, 2014. Blind spectral deconvolution algorithm for Raman spectrum with Poisson noise. Photonics Research, 2, 168--171.

[20]

H. Liu, Z. Zhang, and S. Liu. 2015. Spectral blind deconvolution with differential entropy regularization for infrared spectrum. Infrared Physics & Technology, vol. 71, pp. 481--491.

[21]

J.M.P. Martinez. 2008. Integrating Data Warehouses with Web Data: A Survey, IEEE Trans. Knowledge and Data Eng., 20, 7, 940--955.

Digital Library

[22]

H. Liu, and D. Kong. 2017. Cloud-Class Blended Learning Pattern Innovation and Its Applications. in International Symposium on Educational Technology, 19--23.

[23]

T. Liu, H. Liu, Z. Zhang, and S. Liu, 2018. Nonlocal low-rank-based blind deconvolution of Raman spectroscopy for automatic target recognition, Applied Optics, 57, 6461--6469.

[24]

H. Liu, Y. Li, and Z. Zhang, 2018. Blind Poissonian reconstruction algorithm via curvelet regularization for an FTIR spectrometer, Optics Express, 26, 22837--22856.

[25]

T. Liu, Z. Chen, H. Liu, and Z. Zhang, 2018. FTIR spectral imaging enhancement for teacher's facial expressions recognition in the intelligent learning environment, Infrared Physics & Technology, 93, 213--222.

[26]

Q. Liu, Z. Guo, S. Xiao, and H. Yu, 2018. Depth IR spectroscopic data resolution improvement for antibiotics component analysis in critically ill elderly patients, Infrared Physics & Technology, 93, 291--299.

Cited By

Liu TLiu HLi JZhao LLi HWang GFeng T(2024)EFECNet: Efficient infrared facial expression classification via Gaussian label distribution and graph neural networkProceedings of the 5th International Conference on Computer Information and Big Data Applications10.1145/3671151.3671321(974-980)Online publication date: 26-Apr-2024
https://dl.acm.org/doi/10.1145/3671151.3671321
Lin CDoctorarastoo MFlanigan K(2024)Your Actions Talk: Automated Sociometric Analysis Using Kinesics in Human ActivitiesProceedings of the 11th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation10.1145/3671127.3699529(271-278)Online publication date: 29-Oct-2024
https://dl.acm.org/doi/10.1145/3671127.3699529
Alzubi TAlzubi JSingh AAlzubi OSubramanian M(2023)A Multimodal Human-Computer Interaction for Smart Learning SystemInternational Journal of Human–Computer Interaction10.1080/10447318.2023.220675841:3(1718-1728)Online publication date: 4-May-2023
https://doi.org/10.1080/10447318.2023.2206758
Show More Cited By

Index Terms

Multi-modal hand gesture designing in multi-screen touchable teaching system for human-computer interaction
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interaction techniques
      1. Gestural input

Recommendations

Multi-scenario gesture recognition using Kinect
CGAMES '12: Proceedings of the 2012 17th International Conference on Computer Games: AI, Animation, Mobile, Interactive Multimedia, Educational & Serious Games (CGAMES)

Hand gesture recognition (HGR) is an important research topic because some situations require silent communication with sign languages. Computational HGR systems assist silent communication, and help people learn a sign language. In this article, a ...
A Real Time Vision-Based Hand Gesture Interaction
AMS '10: Proceedings of the 2010 Fourth Asia International Conference on Mathematical/Analytical Modelling and Computer Simulation

Moving beyond mouse and keyboard, the evolution of human-computer interaction (HCI) has been an interest research in recent years which witnessed the development from text-based like using a keyboard to graphic user interface (GUI) based on a mouse, ...
A multi-scale descriptor for real time RGB-D hand gesture recognition
Highlights
- A new RGB-D shape-based hand gesture recognition method is proposed.
- A new hand ...
Abstract
The development of depth cameras, e.g., the Kinect sensor, provides new opportunities for human computer interaction (HCI). Although the Kinect sensor has been extensively applied for human tracking, human action recognition and hand ...

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

ICAIP '18: Proceedings of the 2nd International Conference on Advances in Image Processing

June 2018

261 pages

ISBN:9781450364607

DOI:10.1145/3239576

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

In-Cooperation

University of Electronic Science and Technology of China: University of Electronic Science and Technology of China
Southwest Jiaotong University

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 June 2018

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

ICAIP '18

ICAIP '18: 2018 the 2nd International Conference on Advances in Image Processing

June 16 - 18, 2018

Chengdu, China

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

7
Total Citations
View Citations
191
Total Downloads

Downloads (Last 12 months)9
Downloads (Last 6 weeks)1

Reflects downloads up to 01 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Liu TLiu HLi JZhao LLi HWang GFeng T(2024)EFECNet: Efficient infrared facial expression classification via Gaussian label distribution and graph neural networkProceedings of the 5th International Conference on Computer Information and Big Data Applications10.1145/3671151.3671321(974-980)Online publication date: 26-Apr-2024
https://dl.acm.org/doi/10.1145/3671151.3671321
Lin CDoctorarastoo MFlanigan K(2024)Your Actions Talk: Automated Sociometric Analysis Using Kinesics in Human ActivitiesProceedings of the 11th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation10.1145/3671127.3699529(271-278)Online publication date: 29-Oct-2024
https://dl.acm.org/doi/10.1145/3671127.3699529
Alzubi TAlzubi JSingh AAlzubi OSubramanian M(2023)A Multimodal Human-Computer Interaction for Smart Learning SystemInternational Journal of Human–Computer Interaction10.1080/10447318.2023.220675841:3(1718-1728)Online publication date: 4-May-2023
https://doi.org/10.1080/10447318.2023.2206758
Peng ZYang ZXiahou JXie T(2022)Recognizing Teachers’ Hand Gestures for Effective Non-Verbal InteractionApplied Sciences10.3390/app12221171712:22(11717)Online publication date: 18-Nov-2022
https://doi.org/10.3390/app122211717
Beddiar DNini BSabokrou MHadid A(2020)Vision-based human activity recognition: a surveyMultimedia Tools and Applications10.1007/s11042-020-09004-3Online publication date: 15-Aug-2020
https://doi.org/10.1007/s11042-020-09004-3
Liu TChen ZWang X(2019)Automatic Instructional Pointing Gesture Recognition by Machine Learning in the Intelligent Learning EnvironmentProceedings of the 2019 4th International Conference on Distance Education and Learning10.1145/3338147.3338163(153-157)Online publication date: 24-May-2019
https://dl.acm.org/doi/10.1145/3338147.3338163
Liu TChen ZLiu HZhang Z(2018)FTIR spectral imaging enhancement for teacher’s facial expressions recognition in the intelligent learning environmentInfrared Physics & Technology10.1016/j.infrared.2018.07.03593(213-222)Online publication date: Sep-2018
https://doi.org/10.1016/j.infrared.2018.07.035

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten