We are pleased to announce that our article has been accepted in IEEE Transactions on Human-Machine Systems (IEEE THMS, IF: 3.5) after a lengthy peer-review process. The manuscript was submitted on February 3rd, 2021 and accepted on June 8th, 2024.
We would like to send our gratitude to all parties supporting the publication of the manuscript including IEEE THMS editorial team members, all reviewers, Universitas Gadjah Mada (UGM) through their APC Token IEEE program, Universitas Islam Indonesia for supporting the overlength page excess fee, and Sciencemind Lab for their assistance and proofreading effort during the final phase of publication.
📌 The PDF of the paper is free to download: https://doi.org/10.1109/THMS.2024.3413781
Title: Robust Object Selection in Spontaneous Gaze-Controlled Application Using Exponential Moving Average and Hidden Markov Model
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. Unfortunately, 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 exponential 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.
