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 89.60pm 10.59% and 4364pm 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.