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A Comparative Study of Hand Gesture Recognition Devices in the Context of Game Design

Published:10 November 2019Publication History

ABSTRACT

Gesture recognition devices provide a new means for natural human-computer interaction. However, when selecting these devices for games, designers might find it challenging to decide which gesture recognition device will work best. In the present research, we compare three vision-based, hand gesture devices: Leap Motion, Microsoft's Kinect, and Intel's RealSense. We developed a simple hand-gesture based game to evaluate performance, cognitive demand, comfort, and player experience of using these gesture devices. We found that participants' preferred and performed much better using Leap Motion and Kinect compared to using RealSense. Leap Motion also outperformed or was equivalent to Kinect. These findings suggest that not all gesture recognition devices can be suitable for games and that designers need to make better decisions when selecting gesture recognition devices and designing gesture based games to insure the usability, accuracy, and comfort of such games.

References

  1. Mohamed-Ikbel Boulabiar, Gilles Coppin, and Franck Poirier. 2014. The Issues of 3D Hand Gesture and Posture Recognition Using the Kinect. In Human-Computer Interaction. Advanced Interaction Modalities and Techniques, Masaaki Kurosu (Ed.). Springer International Publishing, Cham, 205--214.Google ScholarGoogle Scholar
  2. Diana Carvalho, Maximino Bessa, Luis Magalhães, and Eurico Carrapatoso. 2015. Performance Evaluation of Gesture-based Interaction Between Different Age Groups Using Fitts' Law. In Proceedings of the XVI International Conference on Human Computer Interaction (Interaccion '15). ACM, New York, NY, USA, Article 5, 7 pages. DOI: http://dx.doi.org/10.1145/2829875.2829920Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Harrison Cook, Quang Vinh Nguyen, and Simeon Simoff. 2015. Enabling Finger-Gesture Interaction with Kinect. In Proceedings of the 8th International Symposium on Visual Information Communication and Interaction (VINCI '15). ACM, New York, NY, USA, 152--153. DOI: http://dx.doi.org/10.1145/2801040.2801060Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Ali Erol, George Bebis, Mircea Nicolescu, Richard D. Boyle, and Xander Twombly. 2007. Vision-based hand pose estimation: A review. Computer Vision and Image Understanding 108, 1 (2007), 52 -- 73. DOI: http://dx.doi.org/https: //doi.org/10.1016/j.cviu.2006.10.012 Special Issue on Vision for Human-Computer Interaction.Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. W. Lu, Z. Tong, and J. Chu. 2016. Dynamic Hand Gesture Recognition With Leap Motion Controller. IEEE Signal Processing Letters 23, 9 (Sept 2016), 1188--1192. DOI: http://dx.doi.org/10.1109/LSP.2016.2590470Google ScholarGoogle ScholarCross RefCross Ref
  6. Zhihan Lv, Alaa Halawani, Shengzhong Feng, Shafiq Ur Réhman, and Haibo Li. 2015. Touch-less Interactive Augmented Reality Game on Vision-based Wearable Device. Personal Ubiquitous Comput. 19, 3--4 (July 2015), 551--567. DOI: http://dx.doi.org/10.1007/s00779-015-0844--1Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Christiane Moser and Manfred Tscheligi. 2015. Physics-based Gaming: Exploring Touch vs. Mid-air Gesture Input. In Proceedings of the 14th International Conference on Interaction Design and Children (IDC '15). ACM, New York, NY, USA, 291--294. DOI: http://dx.doi.org/10.1145/2771839.2771899Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Florian 'Floyd' Mueller, Richard Byrne, Josh Andres, and Rakesh Patibanda. 2018. Experiencing the Body As Play. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (CHI '18). ACM, New York, NY, USA, Article 210, 13 pages. DOI: http://dx.doi.org/10.1145/3173574.3173784Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Paulo Peixoto and Joao Carreira. 2005. A Natural Hand Gesture Human Computer Interface using Contour Signatures. (2005).Google ScholarGoogle Scholar
  10. Alexandros Pino, Evangelos Tzemis, Nikolaos Ioannou, and Georgios Kouroupetroglou. 2013. Using Kinect for 2D and 3D Pointing Tasks: Performance Evaluation. In Human-Computer Interaction. Interaction Modalities and Techniques, Masaaki Kurosu (Ed.). Springer Berlin Heidelberg, Berlin, Heidelberg, 358--367.Google ScholarGoogle Scholar
  11. Lawrence Sambrooks and Brett Wilkinson. 2013. Comparison of Gestural, Touch, and Mouse Interaction with Fitts' Law. In Proceedings of the 25th Australian Computer-Human Interaction Conference: Augmentation, Application, Innovation, Collaboration (OzCHI '13). ACM, New York, NY, USA, 119--122. DOI: http://dx.doi.org/10.1145/2541016.2541066Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. M. C. Bessa Seixas, J. C. S. Cardoso, and M. T. G. Dias. 2015. The Leap Motion movement for 2D pointing tasks: Characterisation and comparison to other devices. In 2015 International Conference on Pervasive and Embedded Computing and Communication Systems (PECCS). 15--24.Google ScholarGoogle Scholar
  13. J. Svoboda, M. M. Bronstein, and M. Drahansky. 2015. Contactless biometric hand geometry recognition using a low-cost 3D camera. In 2015 International Conference on Biometrics (ICB). 452--457. DOI: http://dx.doi.org/10.1109/ICB.2015.7139109Google ScholarGoogle ScholarCross RefCross Ref
  14. Aaron Tabor, Scott Bateman, Erik Scheme, David R. Flatla, and Kathrin Gerling. 2017. Designing Game-Based Myoelectric Prosthesis Training. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (CHI '17). ACM, New York, NY, USA, 1352--1363. DOI: http://dx.doi.org/10.1145/3025453.3025676Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. L. Vokorokos, J. Mihal'ov, and E. Chovancová. 2016. Motion sensors: Gesticulation efficiency across multiple platforms. In 2016 IEEE 20th Jubilee International Conference on Intelligent Engineering Systems (INES). 293--298. DOI: http://dx.doi.org/10.1109/INES.2016.7555139Google ScholarGoogle ScholarCross RefCross Ref
  16. I. Vrellis, A. Moutsioulis, and T. A. Mikropoulos. 2014. Primary School Students' Attitude towards Gesture Based Interaction: A Comparison between Microsoft Kinect and Mouse. In 2014 IEEE 14th International Conference on Advanced Learning Technologies. 678--682. DOI: http://dx.doi.org/10.1109/ICALT.2014.199Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Juan Pablo Wachs, Mathias Kölsch, Helman Stern, and Yael Edan. 2011. Vision-based Hand-gesture Applications. Commun. ACM 54, 2 (Feb. 2011), 60--71. DOI:http://dx.doi.org/10.1145/1897816.1897838Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Frank Weichert, Daniel Bachmann, Bartholomäus Rudak, and Denis Fisseler. 2013. Analysis of the Accuracy and Robustness of the Leap Motion Controller. Sensors 13, 5 (2013), 6380--6393. DOI: http://dx.doi.org/10.3390/s130506380Google ScholarGoogle ScholarCross RefCross Ref
  19. Yuan Yao, Po-Tsung Chiu, and Wai-Tat Fu. 2017. A Gestural Interface for Practicing Children's Spatial Skills. In Proceedings of the 22Nd International Conference on Intelligent User Interfaces Companion (IUI '17 Companion). ACM, New York, NY, USA, 43--47. DOI:http://dx.doi.org/10.1145/3030024.3038265Google ScholarGoogle ScholarDigital LibraryDigital Library

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    • Published in

      cover image ACM Conferences
      ISS '19: Proceedings of the 2019 ACM International Conference on Interactive Surfaces and Spaces
      November 2019
      450 pages
      ISBN:9781450368919
      DOI:10.1145/3343055

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      • Published: 10 November 2019

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      ISS '19 Paper Acceptance Rate26of85submissions,31%Overall Acceptance Rate147of533submissions,28%

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