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The issue of study in emotion or affect has been recently examined by Human-computer Interaction (HCI) research groups, in particular for the development of affective interaction and design. With the recent technological advances, humans are able to interact with computers in ways which are almost impossible. The new modalities for computer interac...
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Context 1
... system is developed using visual programming tool, featuring the affective interface design framework. The framework is architectured from underlying model of integrated methods i.e. Cognitive Task The storyboard is reviewed by the subject matter expert (SME) and the client. It serves as the central document of e Learning development. Fig. 2 shows the communication and content flow design process in SCOUT. Storyboarding in SCOUT offers a method for developing initial rough ideas for task design, motivated by [21]. The need for it arose from an inability to get a working prototype off the ground through sketching interface schematics. When the design is fully feasible ...
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Background
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Citations
... Eye tracking refers to the process of measuring and recording the subjects' eye movements while they observe experimental materials; it has a wide range of applications in the field of human visual cognition [36][37][38][39]. An analysis of subjects' eye-tracking index data can accurately reflect the visual information selected by those subjects and reveal their mental activities during viewing and selection [40][41][42][43]. To achieve an efficient comparative analysis of eye-tracking data, we focused on specific areas called areas of interest (AOI) [44]. ...
The high-speed railway station symbol system, generated from regional culture translations, not only improves transfer efficiency but also reveals the area’s unique urban cultural context. In this study, we used an eye-tracking technique and virtual reality technology to examine the visual cognitive preferences of the existing cultural translation method used by the Beijing–Zhangjiakou high-speed railway. Then, considering the design and layout of the existing station symbol system, we analyzed the visual saliency of different elements such as images, words, and symbols in three types of spaces in the Taizicheng high-speed railway station. The experiment site was located in the physical laboratory of the School of Architecture and Design at Beijing Jiaotong University. A total of 94 students from different majors were selected to participate in the experiment, with 92 datapoints eventually being deemed valid. The experiment data showed the following. First, the overall significance ranking of three scenes in the Taizicheng station was: S1 (81.10%) > S2 (64.57%) > S3 (49.57%). The cognitive correctness rankings of the number positions of the three scenes were: S1: 5 > 2 > 3 > 1 = 4; S2: 4 > 2 > 3 > 1 > 5; S3: 1 > 3 > 2 > 5 > 4. Second, the significance ranking of the transliteration element in S1 was: Images > Words > Sculptures > Patterns > Colors; S2 was: Patterns > Colors > Words > Images > Sculptures; and S3 was: Colors > Images > Words > Patterns > Sculptures. The results underscore the validity of the Beijing–Zhangjiakou cultural translation and offer a reference for station layout and spatial optimization. Finally, they provide new ideas for the design and layout of station symbol systems.
... Researcher used eye movement analysis in their study of advertisement layout, they found that participants' fixation point firstly focused on the informative area ( Wedel and Pieters 2002), and found that participants gaze at important area longer in his study of advertisement layout through eye movement analysis (Duchowski 2002(Duchowski , 2003. Further summarized the applications of different emotional physiological signals in the field of human-computer interaction design, and pointed out the significance of emotional physiological signal in usability evaluation part of interactive system (Yusoff and Salim 2010). Therefore, using people's visual physiology characteristic to study eye movement in related disciplines can obtain people's saccade choice and gazing process when they observing various information, so that we can study human's visual perception and integrated process of mental judgment. ...
In order to improve people's living quality and ameliorate their living environment, making the full use of the environmental characteristics of wood materials in the building and interior decoration become more and more important. In this paper, the development and application of eye tracking technology is summarized, and the development trend of eye movement research in the field of wood environment is reviewed. Through an eye tracking experiment research on indoor wood decoration space to explore if different specialty, the sex, the wooden interior proportion factor have significant effect on people's judgment on the picture preference or not. Make exploration of people for different wood interior decoration environment preference and visual cognitive style, providing new ideas and methods for room decoration design. On the other side, through the study of the eye movement to explore the wood interior decoration environment has an objective and effective and scientific significance.
... The first aspect evaluated whether the names of the recognized emotion or emotions are correct or not. From the second aspect, it evaluated the system by the dominance percentage of detected emotions [34,35]. For example, consider a user with two emotions: Joy-50 % and Fear-20 %. ...
To date, most of the human emotion recognition systems are intended to sense the emotions and their dominance individually. This paper discusses a fuzzy model for multilevel affective computing based on the dominance dimensional model of emotions. This model can detect any other possible emotions simultaneously at the time of recognition. One hundred and thirty volunteers from various countries with different cultural backgrounds were selected to record their emotional states. These volunteers have been selected from various races and different geographical locations. Twenty-seven different emotions with their strengths in a scale of 5 were questioned through a survey. Recorded emotions were analyzed with the other possible emotions and their levels of dominance to build the fuzzy model. Then this model was integrated into a fuzzy emotion recognition system using three input devices of mouse, keyboard and the touch screen display. Support vector machine classifier detected the other possible emotions of the users along with the directly sensed emotion. The binary system (non-fuzzy) sensed emotions with an incredible accuracy of 93 %. However, it only could sense limited emotions. By integrating this model, the system was able to detect more possible emotions at a time with slightly lower recognition accuracy of 86 %. The recorded false positive rates of this model for four emotions were measured at 16.7 %. The resulted accuracy and its false positive rate are among the top three accurate human emotion recognition (affective computing) systems.
... The accuracy of the emotions detection was evaluated from 2 different aspects. The first aspect evaluated whether the name of the recognized emotion or emotions are correct or not, and in the second aspect, it evaluated the system by the percentage of detected emotions [14,15]. For example, consider a user with 2 emotions of Joy -50% and Fear -20%. ...
This paper discusses a fuzzy model for multi-level human emotions recognition by computer systems through keyboard keystrokes, mouse and touchscreen interactions. This model can also be used to detect the other possible emotions at the time of recognition. Accuracy measurements of human emotions by the
fuzzy model are discussed through two methods; the first is accuracy analysis and the second is false positive rate analysis. This fuzzy model detects more emotions, but on the other hand, for some of emotions, a lower accuracy was obtained with the comparison with the non-fuzzy human emotions detection
methods. This system was trained and tested by Support Vector Machine (SVM) to recognize the users' emotions. Overall, this model represents a closer similarity between human brain detection of emotions and computer systems.
The emotional experience is present in all types of interaction between human beings. Therefore, it is vital to consider the emotional experience during the design process of users’ interfaces to inspire greater confidence among users as well as for the interface to be easier to learn and use. Considering the complexity of the subject, it is necessary to combine methods to minimize the detection of false positives in the evaluation of the user’s emotional state while interacting with information systems. This work presents a hybrid evaluation approach based on the emotion model described by Scherer [1]. The proposed approach allows designers to check whether the information system creates a positive, neutral or negative emotional reaction in the users. A feasibility study was conducted in which an emotional evaluation of a web system was performed based on a group of elderly users using tablet devices.
Automated stress detection is important in developing adaptive e-learning systems. Empirical evidence suggests that mouse dynamics and keyboard dynamics analyses can be both effective in user behaviour modelling as well as emotion detection compared to physiological measures and facial expression recognitions, and yet they are far less expensive and considered non-intrusive. This paper investigates how mouse dynamics and keyboard dynamics can be affected by emotional stress, particularly stress induced by time pressure, text length and language familiarity. Our research findings show that longer text and unfamiliar language raise users' stress perceptions. Demanding job such as long typing task could result in anomalous behaviours once the users have lost motivation. Language familiarity mainly affects keyboard behaviour but text length change mouse behaviour. This shows that there is a good potential of developing an adaptive e-learning system by detecting learners' emotional stress from keyboard and mouse input.
There have been lots of tools for cognitive neuroscience research and some of them such as eye tracking, EEG grow up quickly. However, as each tool can only complete single function and most of technologies are not very mature, there is still a long road to get a convincing conclusion. In view of that, this paper proposes a tri-modal Schema combining EEG, eye tracking and image recognition for cognitive neuroscience research. The Schema contains the architecture to build such an experiment environment and the steps of data analysis. How to acquiring a signal with few artifacts and synchronize all the signals are focused. It provides a robust solution for establish an experiment environment quickly.
