| Penulis/Author |
Suatmi Murnani (1); Ir. Noor Akhmad Setiawan, S.T., M.T., Ph.D., IPM. (2); Dr.Eng. Ir. Sunu Wibirama, S.T., M.Eng., IPM. (3) |
| Abstrak/Abstract |
The human gaze is a promising input modality for
interactive applications due to its advantages: giving benefits to
motion-impaired people while providing faster, intuitive, and easy
interaction. The most common form of gaze interaction is object
selection. During the last decade, gaze gestures and smooth pursuit-
based interaction have been emerging techniques for spontaneous
object selection in various gaze-controlled applications. Unfor-
tunately, the challenge of spontaneous interaction demands no
prior gaze-to-screen calibration, which leads to inaccurate object
selection. To overcome the accuracy issue, this article proposes a
novel method for spontaneous gaze interaction based on Pearson
product-moment correlation as a measure of similarity, an expo-
nential moving average filter for signal denoising, and a hidden
Markov model to perform eye movement classification. Based on
experimental results, our approach yielded the best object selection
accuracy and success time of 89.60 ± 10.59% and 4364 ± 235.86
ms, respectively. Our results imply that spontaneous interaction for
gaze-controlled applications is possible with careful consideration
of the underlying techniques to handle noisy data generated by the
eye tracker. Furthermore, the proposed method is promising for
future development of interactive touchless display systems that
comply with the health protocols of the World Health Organization
during the COVID-19 pandemic |
