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Motion-cuing algorithms: characterization of users' perception
Human Factors
|March 21, 2015
Summary
Human perception of motion-cuing algorithms (MCAs) is primarily driven by signal correlation and delay, not motion magnitude. This research aids in developing better vehicle simulators and tuning MCAs.
Area of Science:
- Human-Computer Interaction
- Simulation Technology
- Motion Perception
Background:
- Motion-cuing algorithms (MCAs) lack recent advancements due to difficulties in direct comparison.
- Human evaluation and slow tuning processes hinder MCA progress.
Purpose of the Study:
- To characterize human responses to MCAs.
- To compare user perception with objective indicators of MCA performance.
Main Methods:
- 90 participants evaluated vehicle simulators (driving, speedboat) on 3 and 6 degrees of freedom (DoF) platforms.
- The classical washout algorithm was used as a benchmark.
- Subjective user perception was compared against objective MCA performance indicators.
Main Results:
- User sensitivity is higher to correlation and delay than motion magnitude.
- Specific force is more critical than angular speed in driving simulators; the reverse is true for speedboat simulators.
- A 6-DoF simulator offers a marginal improvement over a 3-DoF platform.
Conclusions:
- Human response to MCAs is mainly dictated by normalized Pearson correlation between algorithm input and output signals.
- This validates MCA strategies like signal downscaling and frequency spectrum alteration.
- Findings support future automated tuning and evaluation of MCAs.
