| Penulis/Author |
RAHMAT ADITYA WARMAN (1) ; Dr.Eng. Ir. Sunu Wibirama, S.T., M.Eng., IPM. (2); Ir. Agus Bejo, S.T., M.Eng., D.Eng., IPM. (3) |
| Abstrak/Abstract |
Noise that appears during eye movements data
recording can cause inaccuracy in data readout. Various signal
processing filters can be used to remove this noise, particularly
during smooth pursuit eye movements. However, performance
comparison of those signal processing filters is yet to be known
when they are implemented in a smooth pursuit-based calibration
method. In this study, we compared three signal processing filters
namely Moving Average, Gaussian and Kalman filters to remove
noises in smooth pursuit eye movements. In the experiment,
we compared the performance of Moving Average, Gaussian,
and Kalman filters. From the experimental results, Moving
Average filter yielded errors of 36.97 + 10.62 pixel (horizontal
position) and 48.07 + 15.11 pixel (vertical position). Gaussian
filter yielded errors of 37.74 + 11.23 pixel (horizontal position)
and 51.06 + 17.62 pixel (vertical position). Kalman filter yielded
errors of 56.06 + 30.97 pixel (horizontal position) and 72.98 +
41.21 pixel (vertical position). Experimental results show that
Moving Average filter yielded the best accuracy compared with
the other signal processing filters. In future, our results maybe
used in development of unobtrusive calibration procedure for
spontaneous gaze-based interaction. |
