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Quantifying the Effects of Reduced Update Rate on Motor Performance and User Experience

  • Sung Hun Sim
  • Bing Wu
  • Kyle Brady
  • Andinet Enquobahrie
  • Ricardo Ortiz
  • Sreekanth Arikatla
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 486)

Abstract

We report two experiments that investigated the impact of reduced visual updating speed on users’ motor performance in a Fitts’-law task. The update rate of visual feedback was set between 10 and 30 Hz. In Experiment 1, the trials were blocked by update rate, allowing participants to get adapted to the reduced visual feedback. In Experiment 2, all trials of different update rates were intermixed and presented in random order. Both experiments found that movement time increased with decreasing update rate. Regression analyses revealed that the Fitts’-law model could be extended to accommodate the findings by including a multiplicative component of frame interval (reciprocal of update rate). The participants’ subjective experience reduced rapidly when the update rate was lower than 20 Hz, and the rating data could be modelled using movement time. The results were discussed in the context of implications for developing VR/AR applications.

Keywords

Human performance modeling Update rate Fitts’ law Feedback delay 

Notes

Acknowledgments

The work was supported in part by NIH grants 5R00EB008710 & 9R44OD018334.

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Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  • Sung Hun Sim
    • 1
  • Bing Wu
    • 1
  • Kyle Brady
    • 1
  • Andinet Enquobahrie
    • 2
  • Ricardo Ortiz
    • 2
  • Sreekanth Arikatla
    • 2
  1. 1.Human Systems Engineering ProgramArizona State UniversityMesaUSA
  2. 2.Medical Computing TeamKitware Inc.CarrboroUSA

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