Abstract
The author proposes “games that can be played in a virtual world through the same movements in the real world without installation of sensors and a special controller.” In particular, this chapter introduces a virtual 3D block-building play system that can control the movements with human gestures. This system requires only two small inexpensive RGB high-speed cameras as the peripheral devices. It is not necessary to attach reflective markers on the body of the user. The core of this technology is a hand pose estimation that restores 3D postures from 2D hand images. The technology, which is a so-called data glove without glove, can determine the poses of human hands and fingers or joint angles seamlessly. It is not a pointing device where specific movements are assigned to specific functions of a game. A user therefore does not have to master the instruction operations by the hands in advance to use the functions of the game. He can operate a virtual game by behaving in a manner similar to the daily routine movements as in the physical world. In this chapter, the author also introduces the 3D modeling system based on hand gesture (hand gesture-based CAD system) using the depth sensor for recognizing hand poses.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Recommended Reading
J.M. Rehg, T. Kanade, Visual tracking of high DOF articulated structures: an application to human hand tracking, European Conference on Computer Vision, 1994. pp. 35–46
Y. Kameda, M. Minoh, A human motion estimation method using 3-successive video frames, Proceedings of the virtual systems and multimedia, 1996. pp. 135–140
S. Lu, D. Metaxas, D. Samaras, J. Oliensis, Using multiple cues for hand tracking and model refinement, Proceedings of the CVPR2003, 2003. 2: pp. 443–450
T. Gumpp, P. Azad, K. Welke, E. Oztop, R. Dillmann, G. Cheng, Unconstrained real-time markerless hand tracking for humanoid interaction, Proceedings of the IEEE-RAS international conference on humanoid robots, 2006. CD-ROM
V. Athitos, S. Scarloff, An appearance-based framework for 3D hand shape classification and camera viewpoint estimation, Proceedings automatic face and gesture recognition, 2002. pp. 40–45
K. Hoshino, T. Tanimoto, Real time search for similar hand images from database for robotic hand control. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E88-A(10), 2514–2520 (2005)
Y. Wu, J. Lin, T.S. Huang, Analyzing and capturing articulated hand motion in image sequence. IEEE Trans. Pattern Anal. Machine Intell. 27, 1910–1922 (2005)
K. Hoshino, E. Tamaki, T. Tanimoto, Copycat hand – robot hand imitating human motions at high speed and with high accuracy. Advanced Robotics 21(15), 1743–1761 (2007)
K. Hoshino, T. Kasahara, M. Tomida, T. Tanimoto, Gesture-world environment technology for mobile manipulation – remote control system of a robot with hand pose estimation. Journal of Robotics and Mechatronics 24(1), 180–190 (2012)
M. Tomida, K. Hoshino, Wearable device for high-speed hand pose estimation with a miniature camera. J. Robot. Mech. 27(2), 167–173 (2015)
I. Oikonomidis, N. Kyriazis, A.A. Argyros, Efficient model-based 3D tracking of hand articulations using Kinect, Proceedings of the 22nd British Machine Vision Conference (BMVC’2011), 2011. 1: pp. 1–11
K. Hamamatsu, K. Hoshino, Detection of pinching gestures using a depth sensor and its application to 3D modeling, 2013 IEEE/SICE international symposium on system integration, 2013. TA2-K(1): pp. 814–819
K. Hoshino, K. Hamamatsu, Gesture CAD, Proceedings of the 38th international MATADOR conference on advanced manufacturing, 2015. 2(1): pp. 17–22
A.D. Wilson, Robust computer vision-based detection of pinching for one and two-handed gesture input, Proceedings of the UIST’06, 2006. pp. 255–258
K. Fukuchi, T. Sato, H. Mamiya, H. Koike, Pac-pac: pinching gesture recognition for tabletop entertainment system, Proceedings of the international conference on advanced visual interfaces, 2010. pp. 267–273
M. Endo, The inseparable pen and sword – study in spirit and in practice. J. Japanese Soc. Biofeedback Res. 42(2) 2015, printing
D. Kim, O. Hilliges, S. Izadi, A. Butler, J. Chen, I. Oikonomidis, P. Olivier, Digits: freehand 3D interactions anywhere using a wrist-worn gloveless sensor, UIST’12 proceedings of the 25th annual ACM symposium on user interface and software technology, 2012. pp. 167–176
L.A.F. Fernandes, V.F. Pamplona, J.L. Prauchner, L.P. Nedel, M.M. Oliveira, Conceptual image-based data glove for computer-human interaction. Rev. Inf. Teórica Aplicada (RITA) 15(3), 75–94 (2008)
R. Fukui, M. Watanabe, T. Gyota, M. Shimosaka, T. Sato, Hand shape classification with a wrist contour sensor: development of a prototype device, UbiComp ’11, 2011. pp. 311–314
K. Hoshino, Ultraminiature camera-based hand pose estimation. Proceedings of the 18th International Conference on Mechatronics Technology (ICMT), 2014, 18(77): pp. 1–8
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Science+Business Media Singapore
About this entry
Cite this entry
Hoshino, K. (2017). Hand Gesture Interface for Entertainment Games. In: Nakatsu, R., Rauterberg, M., Ciancarini, P. (eds) Handbook of Digital Games and Entertainment Technologies. Springer, Singapore. https://doi.org/10.1007/978-981-4560-50-4_47
Download citation
DOI: https://doi.org/10.1007/978-981-4560-50-4_47
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-4560-49-8
Online ISBN: 978-981-4560-50-4
eBook Packages: EngineeringReference Module Computer Science and Engineering