Abstract
Vision constitutes a significant part of information input for Human Machine Interaction (HMI), and the understanding of gaze characteristics such as stability, focus, and duration is promising to design a good User Interface (UI). This paper defines HMI events as UI design factors and identifies its correlation with users’ feedback, i.e. Gaze Metrics (GM) and affect. The observation of the trilateral relationship between these parameters during a pilot testing offers insights to designers on how to improve UI design to enhance usability and attractiveness.
Highlights
This study proposes a concept to use Gaze Metrics (GM) as the objective measure to bridge Human Machine Interaction (HMI) with Delight Design, which is a design methodology to fulfil the ‘excitement’ of Kano model (Verduyn, 2014)
The relationship between User Interface (UI) design factors and gaze behaviour remains ambiguous. This study addresses these gaps by mapping GM to UI design factors and making comparisons with the results of the conventional method using Semantic Differential (SD)
The dependent variable of the Multiple Linear Regression (MLR) model is rearranged to be the intercept of the linear equation and it is plotted as lines on Figure 1(a)
Summary
This study proposes a concept to use GM as the objective measure to bridge HMI with Delight Design, which is a design methodology to fulfil the ‘excitement’ of Kano model (Verduyn, 2014). Self-reported measures such as questionnaires and Semantic Differential (SD) are commonly used to evaluate users’ feedback These methods are susceptible to bias such as social masking and cultural differences (Jang, 2015). The relationship between UI design factors and gaze behaviour remains ambiguous. This study addresses these gaps by mapping GM to UI design factors and making comparisons with the results of the conventional method using SD. As far as we understand, this trilateral relationship among UI design factors, psychology and physiology has yet to be studied This concept (1) facilitates the evaluation of the feasibility to use GM to reduce subjective bias, and (2) reduces the ambiguity between UI design factors and users’ response such as gaze behaviour and ‘excitement’
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