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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

                Contributors
                Conference
                July 2016
                July 2016
                : 1-3
                Affiliations
                University of Tokyo

                Bunkyo-ku, 7-3-1 Hongo
                Article
                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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