Using Kinect for 2D and 3D Pointing Tasks: Performance Evaluation

  • Alexandros Pino
  • Evangelos Tzemis
  • Nikolaos Ioannou
  • Georgios Kouroupetroglou
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8007)


We present a study to comparatively evaluate the performance of computer-based 2D and 3D pointing tasks. In our experiments, based on the ISO 9241-9 standard methodology, a Microsoft Kinect device and a mouse were used by seven participants. For the 3D experiments we introduced a novel experiment layout, supplementing the ISO. We examine the pointing devices’ conformance to Fitts’ law and we measure a number of extra parameters that describe more accurately the cursor movement trajectories. Throughput, measured in bits per second is the most important performance measure. For the 2D tasks using Microsoft Kinect, Throughput is almost 39% lower than using the mouse, Target Re-Entry is 10 times up and Missed Clicks count is almost 50% higher. However, for the 3D tasks the mouse has a 9% lower Throughput than the Kinect, while Target Re-Entry and Missed Clicks are almost identical. Our results are also compared to older studies, and we finally show that the Kinect, operated by the user’s hand and voice, is a suitable and effective input method for pointing and clicking, especially in 3D tasks.


Fitts’ law 3D pointing ISO 9241-9 Microsoft Kinect Gesture User Interface 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Microsoft: Kinect - Xbox 360+Kinect homepage,
  2. 2.
    Kela, J., Korpipää, P., Mäntyjärvi, J., Kallio, S., Savino, G., Jozzo, L., Marca, D.: Accelerometer-Based Gesture Control for a Design Environment. Pers. Ubiquit. Comput. 10(5), 285–299 (2006)CrossRefGoogle Scholar
  3. 3.
    Kratz, S., Rohs, M.: The $3 recognizer: Simple 3D Gesture Recognition on Mobile Devices. In: IUI 2010, 15th International Conference on Intelligent User Interfaces, pp. 419–420. ACM Press, New York (2010)Google Scholar
  4. 4.
    Marvel, J.A., Franaszek, M., Wilson, J., Hong, T.H.: Performance Evaluation of Consumer-Grade 3D Sensors for Static 6DOF Pose Estimation Systems. In: Tescher, A.G. (ed.) Applications of Digital Image Processing XXXV, SPIE Optical Engineering and Applications Conference, vol. 8499, article 849905. SPIE, Bellingham (2012)Google Scholar
  5. 5.
    Oikonomidis, I., Kyriazis, N., Argyros, A.A.: Efficient Model-Based 3D Tracking of Hand Articulations Using Kinect. In: Hoey, J., McKenna, S., Trucco, E. (eds.) 22nd British Machine Vision Conference, pp. 101.1–101.11. BMVA Press, Manchester (2011)Google Scholar
  6. 6.
    Oikonomidis, I., Kyriazis, N., Argyros, A.A.: Tracking the Articulated Motion of Two Strongly Interacting Hands. In: CVPR 2012, IEEE Conference on Computer Vision and Pattern Recognition, pp. 1862–1869. IEEE (2012)Google Scholar
  7. 7.
    Fitts, P.M.: The information capacity of the human motor system in controlling the amplitude of movement. J. Exp. Psychol. 47(6), 381–391 (1954)CrossRefGoogle Scholar
  8. 8.
    Murata, A., Iwase, H.: Extending Fitts’ law to a three-dimensional pointing task. Hum. Mov. Sci. 20, 791–805 (2001)CrossRefGoogle Scholar
  9. 9.
    Foehrenbach, S., König, W.A., Gerken, J., Reiterer, H.: Tactile Feedback Enhanced Hand Gesture Interaction at Large, High-resolution Displays. J. Visual Lang. Comput. 20(5), 341–351 (2009)CrossRefGoogle Scholar
  10. 10.
    Fitts, P.M., Peterson, J.R.: Information capacity of discrete motor responses. J. Exp. Psychol. 67(2), 103–112 (1964)CrossRefGoogle Scholar
  11. 11.
    Pino, A., Kalogeros, E., Salemis, I., Kouroupetroglou, G.: Brain Computer Interface Cursor Measures for Motion-impaired and Able-bodied Users. In: Stephanidis, C. (ed.) HCI International 2003. Universal Access in HCI: Inclusive Design in the Information Society, vol. 4, pp. 1462–1466. Lawrence Erlbaum Associates, Mahwah (2003)Google Scholar
  12. 12.
    ISO 9241-9:2000: Ergonomic Requirements for Office Work with Visual Display Terminals (VDTs) - Part 9: Requirements for Non-keyboard Input Devices. ISO Standard (2000)Google Scholar
  13. 13.
    Soukoreff, W.R., MacKenzie, I.S.: Towards a standard for pointing device evaluation, perspectives on 27 years of Fitts’ law research in HCI. Int. J. Hum.-Comput. St. 61(6), 751–789 (2004)CrossRefGoogle Scholar
  14. 14.
    MacKenzie, I.S., Kauppinen, T., Silfverberg, M.: Accuracy measures for evaluating computer pointing devices. In: CHI 2001, SIGCHI Conference on Human Factors in Computing Systems, pp. 9–16. ACM Press, New York (2001)Google Scholar
  15. 15.
    Keates, S., Hwang, F., Langdon, P., Clarkson, J.P.: Cursor measures for motion-impaired computer users. In: ASSETS 2002, the 5th International ACM Conference on Assistive Technologies, pp. 135–142. ACM Press, New York (2002)Google Scholar
  16. 16.
    Kouroupetroglou, G., Pino, A., Balmpakakis, A., Chalastanis, D., Golematis, V., Ioannou, N., Koutsoumpas, I.: Using Wiimote for 2D and 3D Pointing Tasks: Gesture Performance Evaluation. In: Efthimiou, E., Kouroupetroglou, G., Fotinea, S.-E. (eds.) GW 2011. LNCS (LNAI), vol. 7206, pp. 13–23. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  17. 17.
    National Instruments: NI LabVIEW – Improving the Productivity of Engineers and Scientists. LabVIEW System Design Software homepage,

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Alexandros Pino
    • 1
  • Evangelos Tzemis
    • 2
    • 3
  • Nikolaos Ioannou
    • 2
  • Georgios Kouroupetroglou
    • 1
    • 2
  1. 1.Accessibility Unit for Students with DisabilitiesNational and Kapodistrian University of AthensAthensGreece
  2. 2.Department of Informatics and TelecommunicationsNational and Kapodistrian University of AthensAthensGreece
  3. 3.Department of Computer ScienceUniversity of CopenhagenCopenhagenDenmark

Personalised recommendations