To test the performance of our fixed-gaze head movement detection algorithm and the acceptance of triggering commands by these movements when the user's hands are occupied by another task, we have designed and developed an experimental application known as EyeMusic. In this instance, we have proposed a new algorithm for fixed-gaze head movement detection using only scene images captured by the scene camera equipped in front of the head-mounted eye-tracker, for the purpose of saving computation time. In our research, we investigate the combination of eye-tracking and fixed-gaze head movement, which allows us to trigger various commands without using our hands or changing gaze direction. However, it lacks convenient action triggering methods.
Our research sets the boundaries for more comfortable and easier interaction with notifications and discusses implications for target selections in AR while cycling.Įye-tracking has a very strong potential in human computer interaction (HCI) as an input modality, particularly in mobile situations. Participants favored the MAGIC Pointing approach, supporting cyclists in AR selection tasks. Our results show significantly faster response times for MAGIC Pointing compared to Dwell Time and Gestures, while Dwell Time led to a significantly lower error rate compared to Gestures. We assessed the efficiency regarding reaction times, error rates, and perceived task load. In a user study (N=18), participants confirmed notifications in Augmented Reality (AR) using the three interaction modalities in a simulated biking scenario.
MAGIC TRAIL FOR MOUSE MANUAL
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