Glance behavior as design indices of in-vehicle visual support system: A study using crane simulators. (November 2018)

• Record Type: Journal Article
• Authors: Chew, Jouh Yeong; Ohtomi, Koichi; Suzuki, Hiromasa
• Is Part Of: Applied ergonomics. Volume 73(2018)
• Page Start: 183
• Page End: 193
• Publication Date: 2018-11
• Subjects: Gaze metrics -- Dynamic area-of-interests -- In-vehicle visual support system (IVVS) -- Operator distraction -- Subjective response -- Crane operation; Human engineering -- Periodicals; 620.82
• DOI: 10.1016/j.apergo.2018.07.005
• Languages: English
• ISSNs: 0003-6870

Abstract

Abstract: A prediction model is used to predict subjective responses of crane operators with respect to different designs of In-Vehicle Visual Support (IVVS). Selected gaze metrics are used as objective metrics to minimize prejudice, which is commonly caused by subjective measures. Experiments are carried out using crane simulator to measure glance behavior of novice operators and the 3D perspective projection method is used for autonomous mapping of gaze fixations to dynamic Area-of-Interests (AOIs). Subjective responses, such as operators' emotion and usability of IVVS, are evaluated using the Likert scale of the Semantic Differential method. Correlation between gaze metrics and subjective responses is established using multiple linear regression. Glance behavior exhibits a statistically significant difference when information on IVVS is perceived as useful to ease operation and reduce tension. Despite this, there are no significant signs of distraction. Glance behavior is found to be a reliable sub-conscious indicator of subjective response and distraction. More importantly, the proposed gaze metrics are found to be a good representation of glance behavior, such as randomness and distribution of attention. The methods and findings are useful to evaluate impact of IVVS, which is becoming more common in many other applications. Highlights: Gaze behaviour is used to evaluate design of crane's User Interface (UI). Several gaze metrics are proposed to model gaze behaviour. Feature selection method identified gaze metrics to evaluate design of UI. Selected gaze metrics predicted subjective responses of users in terms of emotion and usability of UI. 3D perspective projection is used for autonomous mapping of gaze to dynamic AOIs.