+1 Recommend
1 collections
      • Record: found
      • Abstract: found
      • Conference Proceedings: found
      Is Open Access

      Eye Gaze in HMI to Design a Crane’s UI

      1 , 1 , 1

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


      11 - 15 July 2016

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

      Read this article at

          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.


          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.

          Related collections

          Most cited references 9

          • Record: found
          • Abstract: found
          • Article: found
          Is Open Access

          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.
            • Record: found
            • Abstract: not found
            • Article: not found

            Multimodal Intelligent Eye-Gaze Tracking System

              • Record: found
              • Abstract: not found
              • Conference Proceedings: not found

              Gaze-based interaction on multiple displays in an automotive environment


                Author and article information

                July 2016
                July 2016
                : 1-3
                University of Tokyo

                Bunkyo-ku, 7-3-1 Hongo
                © 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

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


                Comment on this article