510 research outputs found

    Eye Tracking in User Interfaces

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    Tato diplomová práce byla vytvořena během studijního pobytu na Uviversity of Estern Finland, Joensuu, Finsko. Tato diplomová práce se zabývá využitím technologie sledování pohledu neboli také sledování pohybu očí (Eye-Tracking) pro interakci člověk-počítač (Human-Computer Interaction (HCI)). Navržený a realizovaný systém mapuje pozici bodu pohledu/zájmu (the point of gaze), která odpovídá souřadnicím v souřadnicovém systému kamery scény do souřadnicového systému displeje. Zároveň tento systém kompenzuje pohyby uživatele a tím odstraňuje jeden z hlavních problémů využití sledování pohledu v HCI. Toho je dosaženo díky stanovení transformace mezi projektivním prostorem scény a projektivním prostorem displeje. Za použití význačných bodů (interesting points), které jsou nalezeny a popsány pomocí metody SURF, vyhledání a spárování korespondujících bodů a vypočítání homografie. Systém byl testován s využitím testovacích bodů, které byly rozložené po celé ploše displeje.This MSc Thesis was performed during a study stay at the University of Eastern Finland, Joensuu, Finland. This thesis presents the utilization of Eye-Tracking technology in Human-Computer Interaction (HCI). The proposed and implemented system is able to map co-ordinates in the plane of a scene camera, which correspond with co-ordinates of the point of gaze, into co-ordinates in the plane of a display device. In addition, the system compensates user's motions and thus removes one of main problems of use of Eye-Tracking in HCI. This is achieved by determination of a transformation between the projective space of scene and the projective space of display. Method is based on detection and description of interesting points by using SURF, matching of corresponding points and calculating of homography. The system has been tested by using testing points, which are spread over the display area.

    Eye tracking scanpath analysis techniques on web pages: A survey, evaluation and comparison

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    Eye tracking has commonly been used to investigate how users interact with web pages, with the goal of improving their usability. This article comprehensively revisits the techniques that could be applicable to eye tracking data for analysing user scanpaths on web pages. It also uses a third-party eye tracking study to compare these techniques. This allows researchers to recognise existing techniques for their goals, understand how they work and know their strengths and limitations so that they can make an efficient choice for their studies. These techniques can mainly be used for calculating similarities/dissimilarities between scanpaths, computing transition probabilities between web page elements, detecting patterns in scanpaths and identifying common scanpaths. The scanpath analysis techniques are classified into four groups by their goals so that researchers can directly focus on the appropriate techniques for a sequential analysis of user scanpaths on web pages. This article also suggests dealing with the limitations of these techniques by pre-processing eye tracking data, considering cognitive processing and addressing their reductionist approach

    Detection of natural structures and classification of HCI-HPR data using robust forward search algorithm

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    Purpose – The purpose of this paper is to proposes a forward search algorithm for detecting and identifying natural structures arising in human-computer interaction (HCI) and human physiological response (HPR) data. Design/methodology/approach – The paper portrays aspects that are essential to modelling and precision in detection. The methods involves developed algorithm for detecting outliers in data to recognise natural patterns in incessant data such as HCI-HPR data. The detected categorical data are simultaneously labelled based on the data reliance on parametric rules to predictive models used in classification algorithms. Data were also simulated based on multivariate normal distribution method and used to compare and validate the original data. Findings – Results shows that the forward search method provides robust features that are capable of repelling over-fitting in physiological and eye movement data. Research limitations/implications – One of the limitations of the robust forward search algorithm is that when the number of digits for residuals value is more than the expected size for stack flow, it normally yields an error caution; to counter this, the data sets are normally standardized by taking the logarithmic function of the model before running the algorithm. Practical implications – The authors conducted some of the experiments at individual residence which may affect environmental constraints. Originality/value – The novel approach to this method is the detection of outliers for data sets based on the Mahalanobis distances on HCI and HPR. And can also involve a large size of data with p possible parameters. The improvement made to the algorithm is application of more graphical display and rendering of the residual plot

    A task segment framework to study keylogged translation processes

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    The Task Segment Framework (TSF) is a tool to analyze full typing flows of translation tasks as keylogged with timestamps recorded for keydown, keyup, mouse clicks and moves, and actions performed in other applications. The TSF assumes that intentional pauses flag stretches where subjects concentrate on unrecorded cognitive processes such as planning and assessment. The interspersed typing stretches are task segments, with or without text, where basic subtasks may be observed, mainly adding new text, changing existing text, and searching for information. Accumulated experience and planning allow translators to lump strategically similar activities together, in order to spare efforts and task switching costs while maximizing efficiency. Hence, task segments may contain activities of just one such subtask or many. Translation fluency is a key notion of the TSF, operationalized through many indicators such as typing speed, prior pause length, TS (task segment) length in events, text length as full words, number of typos and respites (=mid inter-keystroke intervals), subtask(s), and the like. The approach seems particularly sensitive to translation expertise levels and may be applied with variations to other multilectal mediated communication tasks. This article lays down the conceptual basis of the TSF and summarizes its basic notions and constructs

    Guidelines for Affect Elicitation and Tracking in High Intensity VR Exergaming

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    Nuni-A case study

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    Incorporating Cognitive Neuroscience Techniques to Enhance User Experience Research Practices

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    User Experience (UX) involves every interaction that customers have with products, and it plays a crucial role in determining the success of a product in the market. While there are numerous methods available in literature for assessing UX, they often overlook the emotional aspect of the user\u27s experience. As a result, cognitive neuroscience methods are gaining popularity, but they have certain limitations such as difficulty in collecting neurophysiological data, potential for errors, and lengthy procedures. This article aims to examine the most effective research practices using cognitive neuroscience techniques and develop a standardized procedure for conducting UX research. To achieve this objective, the study conducts a comprehensive review of UX research that employs cognitive neuroscience methods published between 2017 and 2022
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