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Eye Gaze in HMI to Design a Crane’s UI

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Proceedings of the 30th International BCS Human Computer Interaction Conference (HCI)

Fusion

11 - 15 July 2016

Gaze fixation metric, Subjective bias, Psychophysiology, Delight design, Kano model

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      Abstract

      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.

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      Most cited references 9

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      Analysis of physiological signals for recognition of boredom, pain, and surprise emotions

      Background The aim of the study was to examine the differences of boredom, pain, and surprise. In addition to that, it was conducted to propose approaches for emotion recognition based on physiological signals. Methods Three emotions, boredom, pain, and surprise, are induced through the presentation of emotional stimuli and electrocardiography (ECG), electrodermal activity (EDA), skin temperature (SKT), and photoplethysmography (PPG) as physiological signals are measured to collect a dataset from 217 participants when experiencing the emotions. Twenty-seven physiological features are extracted from the signals to classify the three emotions. The discriminant function analysis (DFA) as a statistical method, and five machine learning algorithms (linear discriminant analysis (LDA), classification and regression trees (CART), self-organizing map (SOM), Naïve Bayes algorithm, and support vector machine (SVM)) are used for classifying the emotions. Results The result shows that the difference of physiological responses among emotions is significant in heart rate (HR), skin conductance level (SCL), skin conductance response (SCR), mean skin temperature (meanSKT), blood volume pulse (BVP), and pulse transit time (PTT), and the highest recognition accuracy of 84.7 % is obtained by using DFA. Conclusions This study demonstrates the differences of boredom, pain, and surprise and the best emotion recognizer for the classification of the three emotions by using physiological signals.
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        Multimodal Intelligent Eye-Gaze Tracking System

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          Gaze-based interaction on multiple displays in an automotive environment

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            Author and article information

            Affiliations
            University of Tokyo

            Bunkyo-ku, 7-3-1 Hongo
            Contributors
            Conference
            July 2016
            July 2016
            : 1-3
            10.14236/ewic/HCI2016.96
            © Chew et al. Published by BCS Learning and Development Ltd. Proceedings of British HCI 2016 Conference Fusion, Bournemouth, UK

            This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

            Proceedings of the 30th International BCS Human Computer Interaction Conference
            HCI
            30
            Bournemouth University, Poole, UK
            11 - 15 July 2016
            Electronic Workshops in Computing (eWiC)
            Fusion
            Product
            Product Information: 1477-9358 BCS Learning & Development
            Self URI (journal page): https://ewic.bcs.org/
            Categories
            Electronic Workshops in Computing

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