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70 results for “eye-tracking”
THÖR - eye-tracking
<p><strong>THÖR</strong> is a dataset with human motion trajectory and eye gaze data collected in an indoor environment with accurate ground truth for the position, head orientation, gaze direction, social grouping and goals. THÖR contains sensor data collected by a 3D lidar sensor and involves a mobile robot navigating the space. In comparison to other, our dataset has a larger variety in human motion behaviour, is less noisy, and contains annotations at higher frequencies.</p> <p><strong>THÖR eye-tracking -</strong> data of the participant (Helmet number 9) from the Tobii Glasses included in this dataset. The entire data from the experiment was recorded into two recordings - "Recording011" and "Recording012".</p> <p>The folder "RawData" consists of exported data from Tobii Pro Lab software using a filter called "Tobii-IVT Attention filter" (velocity threshold parameter set to 100 degrees/second), which is a recommended method for dynamic situations. The recommendation was given by the equipment manufactures Tobii Pro and from other researchers. For further information, please refer to https://www.tobiipro.com/siteassets/tobii-pro/user-manuals/Tobii-Pro-Lab-User-Manual/?v=1.86 (Appendix-B, page 85).</p> <p>The recording start times are as follows:<br> Tobii recording011 starttime: 13:34:37.267<br> Tobii recording012 starttime: 14:36:17.730</p> <p>1. "Synchronized_Qualisys_Tobii.mat" file, which consists of all the synchronised data between Qualisys data and Tobii eye-tracker data using timestamps matching. The columns (headers) in this mat file respectively represent - 'timestamp', 'Pos_X', 'Pos_Y', 'Pos_Z', 'Head_R', 'Head_P', 'Head_Y', 'GazepointX', 'GazepointY', 'Gazepoint3D_X', 'Gazepoint3D_Y', 'Gazepoint3D_Z', 'Gazedirectionleft_X', 'Gazedirectionleft_Y', 'Gazedirectionleft_Z', 'Gazedirectionright_X', 'Gazedirectionright_Y', 'Gazedirectionright_Z', 'Pupilpositionleft_X', 'Pupilpositionleft_Y', 'Pupilpositionleft_Z', 'Pupilpositionright_X', 'Pupilpositionright_Y', 'Pupilpositionright_Z', 'Pupildiameterleft', 'Pupildiameterright', 'Gazeeventduration', 'Eyemovementtype_index', 'Fixationpoint_X', 'Fixationpoint_Y', 'Gyro_X', 'Gyro_Y', 'Gyro_Z', 'Accelerometer_X', 'Accelerometer_Y', 'Accelerometer_Z'.</p> <p>2. A matlab script "Synchronizing_Qualisys_Tobii.m" used for matching the timestamps of Qualisys and Tobii and generate a data file "Synchronized_Qualisys_Tobii.mat" that can be found it git repository.</p> <p>3. All the associated data required for running the matlab script.</p> <p>Please note that 'Timestamp', 'Pos_X', 'Pos_Y', 'Pos_Z', 'Head_R', 'Head_P', 'Head_Y','GazepointX', 'GazepointY' represent the timestamps (matched to Tobii timestamps using nearest neighbor search), position and head orientation from Qualisys data and rest of the data is from Tobii eye-tracker. For more information regarding the eye-tracker data, please refer to https://www.tobiipro.com/siteassets/tobii-pro/user-manuals/Tobii-Pro-Lab-User-Manual/?v=1.86 (Section 8.7.2.1, page 68).</p> <p> </p>
A Real-Time Eye-Tracking Dataset for Autism Severity Classification Using Deep Learning
<p>Eye-Tracking (ET) technologies have shown significant potential in autism research, providing critical insights into gaze patterns and their correlation with autism severity. However, a persistent challenge in developing Deep Learning (DL) models for ET analysis is the lack of publicly available, annotated datasets tailored for specific tasks. In order to close this gap, we present a novel, meticulously annotated resource designed to classify autism severity based on ET data. This dataset consists of 4,000 high-resolution (416×416 pixels) eye images derived from video recordings of 40 participants, evenly distributed across four autism severity groups: low, mild, medium, and high.</p> <p>Each participant's video was processed to extract 50 frames per session, capturing diverse gaze behaviors such as fixations, saccades, and smooth pursuits. Both left and right eye images were segmented from these frames, yielding 100 images per participant and ensuring balanced representation across severity categories (1,000 images per group). The dataset is annotated with detailed metadata, including subject ID, frame number, autism severity level, and eye type (left or right), providing a robust foundation for precise feature extraction and analysis.</p> <p><span>Facilitating its application in DL model development, this dataset addresses a critical gap in the limited availability of ET datasets. It provides a robust benchmark for autism severity classification, establishing a foundational resource for advancing Machine Learning(ML) research in the domain of autism</span><span>. This dataset serves as a critical resource for advancing ET-based classification models, fostering accurate and efficient assessment of autism severity, and supporting broader autism research.</span></p>
Atari-HEAD: Atari Human Eye-Tracking and Demonstration Dataset
<p>Version 4 of the dataset is available (Sep 19 2019)!</p> <p>Note this version has significantly more data than Version 2. </p> <p>Dataset description paper (full version) is available!</p> <p>https://arxiv.org/pdf/1903.06754.pdf (updated Sep 7 2019)</p> <p>Tools for visualizing the data is available!</p> <p>https://github.com/corgiTrax/Gaze-Data-Processor</p> <p> </p> <p><strong>=========================== Dataset Description ===========================</strong></p> <p>We provide a large-scale, high-quality dataset of human actions with simultaneously recorded eye movements while humans play Atari video games. The dataset consists of 117 hours of gameplay data from a diverse set of 20 games, with 8 million action demonstrations and 328 million gaze samples. We introduce a novel form of gameplay, in which the human plays in a semi-frame-by-frame manner. This leads to near-optimal game decisions and game scores that are comparable or better than known human records. For every game frame, its corresponding image frame, the human keystroke action, the reaction time to make that action, the gaze positions, and immediate reward returned by the environment were recorded.</p> <p> </p> <p>Q & A: Why frame-by-frame game mode?</p> <p><strong>Resolving state-action mismatch</strong>: Closed-loop human visuomotor reaction time is around 250-300 milliseconds. Therefore, during gameplay, state (image) and action that are simultaneously recorded at time step t could be mismatched. Action at time t could be intended for a state 250-300ms ago. This effect causes a serious issue for supervised learning algorithms, since label at and input st are no longer matched. Frame-by-frame game play ensures states and actions are matched at every timestep.</p> <p><strong>Maximizing human performance</strong>: Frame-by-frame mode makes gameplay more relaxing and reduces fatigue, which could normally result in blinking and would corrupt eye-tracking data. More importantly, this design reduces sub-optimal decisions caused by inattentive blindness.</p> <p><strong>Highlighting critical states that require multiple eye movements</strong>: Human decision time and all eye movements were recorded at every frame. The states that could lead to a large reward or penalty, or the ones that require sophisticated planning, will take longer and require multiple eye movements for the player to make a decision. Stopping gameplay means that the observer can use eye-movements to resolve complex situations. This is important because if the algorithm is going to learn from eye-movements it must contain all “relevant” eye-movements.</p> <p> </p> <p><strong>============================ Readme ============================</strong></p> <p>1. meta_data.csv: meta data for the dataset., including:</p> <ul> <li> <p>GameName: String. Game name. e.g., “alien” indicates the trial is collected for game Alien (15 min time limit). “alien_highscore” is the trajectory collected from the best player’s highest score (2 hour limit). See dataset description paper for details.</p> </li> </ul> <ul> <li> <p>trial_id: Integer. One can use this number to locate the associated .tar.bz2 file and label file.</p> </li> <li> <p>subject_id: Char. Human subject identifiers.</p> </li> <li> <p>load_trial: Integer. 0 indicates that the game starts from scratch. If this field is non-zero, it means that the current trial continues from a saved trial. The number indicates the trial number to look for.</p> </li> <li> <p>highest_score: Integer. The highest game score obtained from this trial.</p> </li> <li> <p>total_frame: Number of image frames in the .tar.bz2 repository.</p> </li> <li> <p>total_game_play_time: Integer. game time in ms. </p> </li> <li> <p>total_episode: Integer. number of episodes in the current trial. An episode terminates when all lives are consumed.</p> </li> <li> <p>avg_error: Float. Average eye-tracking validation error at the end of each trial in visual degree (1 visual degree = 1.44 cm in our experiment). See our paper for the calibration/validation process.</p> </li> <li> <p>max_error: Float. Max eye-tracking validation error. </p> </li> <li> <p>low_sample_rate: Percentage. Percentage of frames with less than 10 gaze samples. The most common reason for this is blinking.</p> </li> <li> <p>frame_averaging: Boolean. The game engine allows one to turn this on or off. When turning on (TRUE), two consecutive frames are averaged, this alleviates screen flickering in some games.</p> </li> <li> <p>fps: Integer. Frame per second when an action key is held down.</p> </li> </ul> <p> </p> <p>2. [game_name].zip files: these include data for each game, including:</p> <p>*.tar.bz2 files: contains game image frames. The filename indicates its trial number.</p> <p>*.txt files: label file for each trial, including:</p> <ul> <li> <p>frame_id: String. The ID of a frame, can be used to locate the corresponding image frame in .tar.bz2 file.</p> </li> <li> <p>episode_id: Integer (not available for some trials). Episode number, starting from 0 for each trial. A trial could contain a single trial or multiple trials.</p> </li> <li> <p>score: Integer (not available for some trials). Current game score for that frame.</p> </li> <li> <p>duration(ms): Integer. Time elapsed until the human player made a decision. </p> </li> <li> <p>unclipped_reward: Integer. Immediate reward returned by the game engine.</p> </li> <li> <p>action: Integer. See action_enums.txt for the mapping. This is consistent with the Arcade Learning Environment setup.</p> </li> <li> <p>gaze_positions: Null/A list of integers: x0,y0,x1,y1,...,xn,yn. Gaze positions for the current frame. Could be null if no gaze. (0,0) is the top-left corner. x: horizontal axis. y: vertical.</p> </li> </ul> <p> </p> <p>3. action_enums.txt: contains integer to action mapping defined by the Arcade Learning Environment. </p> <p> </p> <p><strong>============================ Citation ============================</strong></p> <p>If you use the Atari-HEAD in your research, we ask that you please cite the following:</p> <p>@misc{zhang2019atarihead,</p> <p> title={Atari-HEAD: Atari Human Eye-Tracking and Demonstration Dataset},</p> <p> author={Ruohan Zhang and Calen Walshe and Zhuode Liu and Lin Guan and Karl S. Muller and Jake A. Whritner and Luxin Zhang and Mary M. Hayhoe and Dana H. Ballard},</p> <p> year={2019},</p> <p> eprint={1903.06754},</p> <p> archivePrefix={arXiv},</p> <p> primaryClass={cs.LG}</p> <p>}</p> <p>Zhang, Ruohan, Zhuode Liu, Luxin Zhang, Jake A. Whritner, Karl S. Muller, Mary M. Hayhoe, and Dana H. Ballard. "AGIL: Learning attention from human for visuomotor tasks." In Proceedings of the European Conference on Computer Vision (ECCV), pp. 663-679. 2018.</p> <p>@inproceedings{zhang2018agil,</p> <p> title={AGIL: Learning attention from human for visuomotor tasks},</p> <p> author={Zhang, Ruohan and Liu, Zhuode and Zhang, Luxin and Whritner, Jake A and Muller, Karl S and Hayhoe, Mary M and Ballard, Dana H},</p> <p> booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},</p> <p> pages={663--679},</p> <p> year={2018}</p> <p>}</p> <p><br> <br> </p>
EyeLink 1000 raw eye tracking data - Reading numbers is harder than reading words: An eye-tracking study
<p>Reading Arabic numerals is a fundamentally different activity compared to word reading. This study aimed to investigate the eye movements of normal-reading adults when reading aloud short and long Arabic numerals (with or without a thousand separator) compared to matched-in-length words and pseudowords.</p> <p>This dataset contains the raw data of the article "Reading numbers is harder than reading words: An eye-tracking study" published in the journal <em>Acta Psychologica</em> (https://doi.org/10.1016/j.actpsy.2023.103942)</p>
EEG and eye-tracking data from a go/no-go saccadic task based on facial expression cues
<p>Electroencephalographic (EEG) and eye-tracking data from 20 healthy individuals who performed a go/no-go saccadic task based on facial expression cues aimed at studying error monitoring processes.</p> <p> </p> <p>Version 2 includes only the EEG data, but with triggers of correct and erroneous actions. Version 2 has an error that was corrected for version 3.</p> <p> </p> <p>EEG Triggers</p> <table> <tbody> <tr> <td> <p><span>19</span></p> </td> <td> <p><span>Eye tracker starts recording</span></p> </td> </tr> <tr> <td> <p><span>1</span></p> </td> <td> <p><span>Beginning of each trial</span></p> </td> </tr> <tr> <td> <p><span>200</span></p> </td> <td> <p><span>Gap between neutral and instruction</span></p> </td> </tr> <tr> <td> <p><span>99</span></p> </td> <td> <p><span>Fixation cross between instruction and saccade</span></p> </td> </tr> <tr> <td> <p><span>2</span></p> </td> <td> <p><span>Instruction no-go happy</span></p> </td> </tr> <tr> <td> <p><span>21</span></p> </td> <td> <p><span>Target left no-go happy</span></p> </td> </tr> <tr> <td> <p><span>22</span></p> </td> <td> <p><span>Target right no-go happy</span></p> </td> </tr> <tr> <td> <p><span>121</span></p> </td> <td> <p><span>Response period for no-go happy after target left</span></p> </td> </tr> <tr> <td> <p><span>122</span></p> </td> <td> <p><span>Response period for no-go happy after target right</span></p> </td> </tr> <tr> <td> <p><span>3</span></p> </td> <td> <p><span>Instruction no-go sad</span></p> </td> </tr> <tr> <td> <p><span>31</span></p> </td> <td> <p><span>Target left no-go sad</span></p> </td> </tr> <tr> <td> <p><span>32</span></p> </td> <td> <p><span>Target right no-go sad</span></p> </td> </tr> <tr> <td> <p><span>131</span></p> </td> <td> <p><span>Response period for no-go sad after target left</span></p> </td> </tr> <tr> <td> <p><span>132</span></p> </td> <td> <p><span>Response period for no-go sad after target right</span></p> </td> </tr> <tr> <td> <p><span>4</span></p> </td> <td> <p><span>Instruction pro-right</span></p> </td> </tr> <tr> <td> <p><span>42</span></p> </td> <td> <p><span>Target right pro-right</span></p> </td> </tr> <tr> <td> <p><span>142</span></p> </td> <td> <p><span>Response period for pro-right</span></p> </td> </tr> <tr> <td> <p><span>5</span></p> </td> <td> <p><span>Instruction pro-left</span></p> </td> </tr> <tr> <td> <p><span>51</span></p> </td> <td> <p><span>Target left pro-left</span></p> </td> </tr> <tr> <td> <p><span>151</span></p> </td> <td> <p><span>Response period for pro-left</span></p> </td> </tr> <tr> <td> <p><span>6</span></p> </td> <td> <p><span>Instruction anti-left</span></p> </td> </tr> <tr> <td> <p><span>61</span></p> </td> <td> <p><span>Target left anti-left</span></p> </td> </tr> <tr> <td> <p><span>161</span></p> </td> <td> <p><span>Response period for anti-left</span></p> </td> </tr> <tr> <td> <p><span>7</span></p> </td> <td> <p><span>Instruction anti-right</span></p> </td> </tr> <tr> <td> <p><span>72</span></p> </td> <td> <p><span>Target right anti-right</span></p> </td> </tr> <tr> <td> <p><span>172</span></p> </td> <td> <p><span>Response period for anti-right</span></p> </td> </tr> <tr> <td> <p><span>199</span></p> </td> <td> <p><span>Final fixation period (1.5s black screen after last saccade)</span></p> </td> </tr> <tr> <td> <p><span>190</span></p> </td> <td> <p><span>Eye tracker stops recording</span></p> </td> </tr> <tr> <td> <p><span>proCorr</span></p> </td> <td> <p><span>Beginning of saccade in the correct direction following a pro-saccade intruction</span></p> </td> </tr> <tr> <td> <p><span>proErr</span></p> </td> <td> <p><span>Beginning of saccade in the erroneous direction following a pro-saccade intruction</span></p> </td> </tr> <tr> <td> <p><span>antiCorr</span></p> </td> <td> <p><span>Beginning of saccade in the correct direction following a anti-saccade intruction</span></p> </td> </tr> <tr> <td> <p><span>antiErr</span></p> </td> <td> <p><span>Beginning of saccade in the erroneous direction following a anti-saccade intruction</span></p> </td> </tr> <tr> <td> <p><span>nogoErr</span></p> </td> <td> <p><span>Beginning of saccade in the erroneous direction following a no-go intruction</span></p> </td> </tr> </tbody> </table>
Is the Gaze Behavior During Stair Walking Affected by Pregnancy?-Figure 2. Eye-tracking glasses image showing the gaze location during stair ascent
<p>At each data collection, participants walked the same U-shaped staircase descending a 22- treads (riser: 0.16 m, run: 0.33 m, and width: 1.15 m), making a short U-turn downstairs and ascending back the staircase, one tread at a time (Figure 1). Only the data of stair walking were taken for further analysis. The staircase was equipped with a handrail on one side but none of the participants used it. To monitor the gaze a SensoMotoric Instruments (SMI) eye-tracking glasses (ETG) system (SMI, Inc.) at a frequency of 60 frames per second and 1280x960 pixel picture was used. Calibration was performed using a matrix of 3 points placed on a board in different highs and different horizontal placement. Mean gaze vectors of the right eye (x, y, z) for stair descent and stair ascent were obtained for each data collection session. Gaze vector x, y, z starts at the eye and heads off in mediolateral, up and down, and anterior-posterior direction, respectively (Figure 2) (Haffegee, Alexandrov, & Barrow, 2007; Scheel, & Staadt, 2015).</p>
ETDD70: Eye-Tracking Dyslexia Dataset
<p>The ETDD70 dataset comprises eye-tracking recordings from 70 Czech participants, equally divided into dyslexic and non-dyslexic readers, all aged 9–10 years. The dataset captures eye movements during three text-reading tasks in Czech: syllable reading (Task 1), meaningful text reading (Task 4), and pseudo-text reading (Task 5).</p> <p>This dataset is the result of the project “Diagnostics of Dyslexia Using Eye-Tracking and Artificial Intelligence” conducted by our research team. The project aims to leverage artificial intelligence tools and advanced technical equipment (eye tracking) to more effectively diagnose dyslexia, one of the most common specific learning disorders, and thereby significantly improve re-education strategies for dyslexic students. The primary goal is to develop models that accurately distinguish between dyslexic and non-dyslexic readers based on eye movement patterns recorded during these tasks.</p> <p>Data collection took place between October 2022 and August 2023, adhering to ethical standards. The project was approved by the Research Ethics Committee of Masaryk University in Brno, Czech Republic.</p> <p>Please contact us if you have any questions or feedback at <a href="mailto:nicol.dostalova@mail.muni.cz">nicol.dostalova@mail.muni.cz</a> or at <a href="mailto:svaricek@phil.muni.cz">svaricek@phil.muni.cz</a>.</p> <p>The ETDD70 dataset is freely available for research purposes.</p> <p><strong>PARTICIPANTS</strong></p> <p>The eye-tracking data were captured from 70 participants: 35 dyslexic and 35 non-dyslexic readers. In all cases, participants are elementary school pupils aged 9-10 years (i.e., 4th grade of elementary school). Recruitment of suitable participants was conducted in cooperation with a psychological counseling center, which facilitated the recruitment of pupils diagnosed with dyslexia. The non-dyslexic readers, who showed no symptoms of dyslexia, were recruited in cooperation with the counseling facilities of selected elementary schools. The dataset was collected from October 2022 to August 2023. The legal representatives of all participants were properly informed about the research procedure and agreed to participate in the study, for which they subsequently received compensation.</p> <p><strong>STIMULI</strong></p> <p>We designed three verbal tasks based on standardized paper-based dyslexia diagnostics used in the Czech Republic. These source texts were transferred to a digital version in a controlled form (e.g., amount of text, font size, line spacing, background color, etc.) for the requirements of eye-tracking measurements.</p> <p>Task called <strong>Syllables</strong> contains 90 syllables arranged in a 9 x 10 matrix. The syllables are commonly encountered in the Czech language. The individual rows of syllables were categorized according to syllable composition (based on linguistic aspects) as follows: open syllables with no meaning, i.e., consonant + vowel (e.g., "ta," "na"), closed syllables with a central vowel bearing a meaning, i.e., consonant + vowel + consonant (e.g., "suk," "mák"), meaningless syllables consisting of two consonants (e.g., "vl," "bz"), a meaningless syllable formed by a cluster of two consonants ending in a vowel (e.g., "tle," "mra"), and finally a meaningful syllable formed by a cluster of three consonants with one vowel in the 3rd position (e.g., "mrak," "vlak"). All syllables were presented in black font, with Times New Roman on a gray background. The objective of the task is to read aloud all syllables from left to right and from top to bottom. A fixation cross was placed in the lower right corner for gaze-contingent task closure—when the participant looks at this cross, the recording is automatically terminated.</p> <p>Task called <strong>MeaningfulText</strong> consists of a passage about a young boy who watches a squirrel from his window. This text is intended for elementary school readers in grades 3 and 4. The stimulus text contains a total of seven text lines with six logical sentences. The text is again written in black-colored font with double line spacing on a grey background and the fixation cross in the lower right corner. The aim of the task is to read the entire text aloud.</p> <p>Task called <strong>PseudoText </strong>comprises fictional, meaningless words. This text has a total of seven lines with 15 artificial sentences. The text formatting, as well as the ending fixation cross, are the same as in Task MeaningfulText. The objective of the task is to read the entire text aloud as smoothly as possible.</p> <p><strong>EYE-TRACKING FEATURES</strong></p> <p>The raw eye-tracking data recorded for each task were further processed to extract event-based characteristics—fixations, saccades, and dozens of derived statistical characteristics. The fixations were detected using the i2mc algorithm (Hessels et al., 2017), as it was specifically designed to be noise-robust for measurements in children. The minimum fixation duration was set to 40 ms. The derived characteristics provide additional information about how participants interact with text. These characteristics are divided into whole-task and region-of-interest (ROI) characteristics. While the whole-task characteristics describe the semantics on the global level of the whole screen, the ROI ones characterize the semantics on the local level of a small rectangular area.</p> <p><strong>Feature-based characteristics for each task:</strong></p> <p><strong>Syllables</strong></p> <p>First fixation duration, average fixation duration, number of fixations, number of fixations and saccades without the incoming/outgoing saccade, number of revisits—incoming saccades hitting this ROI from outside.</p> <p><strong>MeaningfulText</strong>, <strong>PseudoText</strong></p> <p>Whole-task (features extracted from the whole trial): number of regressions, ratio of progressive to regressive saccades, average saccadic amplitude, total reading duration, average fixation duration, number of fixations.</p> <p>ROI (features extracted for separated regions of interest, i.e. lines and words): average fixation duration, number of fixations, number of revisits—incoming saccades hitting this ROI from outside, landing position of the first fixation.</p> <p><strong>AI CLASSIFICATION APPROACH</strong></p> <p>The AI-based methods used for the classification of dyslexia are available here: <a title="https://gitlab.fi.muni.cz/xsedmid/dyslex" href="https://gitlab.fi.muni.cz/xsedmid/dyslex" target="_blank" rel="noreferrer noopener">https://gitlab.fi.muni.cz/xsedmid/dyslex</a></p> <p> </p> <p> </p> <p><strong>CITE THIS DATASET</strong></p> <p>Dostalova, N., Svaricek, R., Sedmidubsky, J., Culemann, W., Sasinka, C., Zezula, P., & Cenek, J. (2024). <em>ETDD70: Eye-tracking Dyslexia Dataset </em>[Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.13332134">https://doi.org/10.5281/zenodo.13332134</a></p> <p><strong>CITE THE ASSOCIATED PAPER</strong></p> <p>Sedmidubsky, J., Dostalova, N., Svaricek, R., & Culemann, W. (2024). ETDD70: Eye-tracking dataset for classification of dyslexia using AI-based methods. In <em>Proceedings of the 17th International Conference on Similarity Search and Applications (SISAP)</em> (pp. 1-14). Springer.</p>
COLET: A Dataset for Cognitive workLoad estimation based on Eye-Tracking
<p>Cognitive workload is an important component in performance psychology, ergonomics, and human factors. Unfortunately, benchmarks and publicly available datasets are scarce, making it difficult to establish new approaches and comparative studies. In this work, COLET-COgnitive workLoad state estimation based on Eye-Tracking dataset is presented. Forty-seven (47) individuals' eye movements were monitored as they solved puzzles involving visual search tasks of varying complexity and duration. The authors give an in-depth study of the participants' performance during the experiments while eye and gaze features were derived from low-level eye recorded metrics, and their relationships with the experiment tasks were investigated. Finally, the results from the classification of cognitive workload levels solely based on eye and gaze data, by employing and testing a set of machine learning algorithms are provided. The dataset is made available to the public.</p> <p> </p> <p>Please cite the following work: </p> <p>Ktistakis, E., Skaramagkas, V., Manousos, D., Tachos, N. S., Tripoliti, E., Fotiadis, D. I., & Tsiknakis, M. (2022). Colet: A dataset for cognitive workload estimation based on eye-tracking. <em>Computer Methods and Programs in Biomedicine</em>, 106989. https://doi.org/10.1016/j.cmpb.2022.106989</p>
eSEEd: emotional State Estimation based on Eye-tracking dataset
<p>We present eSEEd- emotional State Estimation based on Eye-tracking database. Eye movements of 48 participants were recorded as they watched 10 emotion evoking videos each of them followed by a neutral video. Participants rated five emotions (tenderness, anger, disgust, sadness, neutral) on a scale from 0 to 10, later translated in terms of emotional arousal and valence levels. Furthermore, each participant filled 3 self-assessment questionnaires. An extensive analysis of the participants' answers to the questionnaires self-assessment scores as well as their ratings during the experiments is presented. Moreover, eye and gaze features were extracted from the low level eye recorded metrics and their correlations with the participants' ratings are investigated. Finally, analysis and results are presented for machine learning approaches, for the classification of various arousal and valence levels based solely on eye and gaze features. The dataset is made publicly available and we encourage other researchers to use it for testing new methods and analytic pipelines for the estimation of an individual's affective state.<br><br>TO USE THIS DATASET PLEASE CITE:<br>Skaramagkas, V.; Ktistakis, E.; Manousos, D.; Kazantzaki, E.; Tachos, N.S.; Tripoliti, E.; Fotiadis, D.I.; Tsiknakis, M. eSEE-d: Emotional State Estimation Based on Eye-Tracking Dataset. <em>Brain Sci.</em> 2023, <em>13</em>, 589. https://doi.org/10.3390/brainsci13040589</p>
Eye-tracking (EOG) Data
<p>Dataset taken from https://sites.google.com/site/consensusmotifs/</p> <p>Stored on Zenodo as backup for Stumpy Consensus Motif Search</p>
A comparative usability analysis of eye-tracking and mouse click data taken from digital libraries
<p>This dataset is the result of a study, in which we analyzed parallels and differences between clicks as well as eye movements on two different digital library homepages. For this analysis we used diverse tracking tools for mouse clicks and eye tracking data that where further studied with respect to specific areas of interest (AOI). </p> <p>The dataset contains two screenshots indicating the areas of interest (AOIs; entitled “AreasOfInterest_Kartenportal.jpg and AreasOfInterest_Webportal.jpg), which separate the homepages into analyzable parts. It also contains eight screenshots of the homepages containing the total amount of collected clicks (each name starting with “clicks”) and two screenshots with the eye tracking heat maps (starting with “Heatmap”). The screenshots have directly been extracted from the click and eye tracking tools and matched with the before mentioned AOIs in order to gain the total count of clicks and views as well as the view duration the concerned area.</p> <p>All data are synthesized in a document containing three sheets with different tables: a first one with the initial data compilation for all AOIs of the two analyzed homepages (entitled “Data”), a second one with a more visual compiled data analysis for both homepages and all AOIs (entitled “Data2) and last one with the duration of the view as well as the duration of the fixation and the compiled click data (entitled « Eye tracking study data »).</p>
"Haptic aesthetics and bodily properties of Ori Gersht's digital art: a behavioral and eye-tracking study." - datasets and script
<p>"Haptic aesthetics and bodily properties of Ori Gersht’s digital art: a behavioral and eye-tracking study."</p> <p>Datasets and R script for analyses of behavioural scores and visual parameter.</p>
Data from an eye-tracking based study of solution strategies in similar mathematical and physical tasks
<p>Eye-tracking data:</p> <p>The .nas file "ET_data" contains the eyetracking project in software tobii studio with gaze data of all 131 participants.<br> To open this project, the software tobii studio is required.</p> <p>----------------------------------------<br> Interview data:</p> <p>All .txt files contain transcribed interview data in German language. <br> The files are labeled by subject number (e.g., P01 for the first participant) and the item for which the interview was recorded (e.g., M8).<br> The files contain interview data of participants explaining their solution strategy for the given item.</p> <p>----------------------------------------<br> Descriptive data:</p> <p>The file descriptive_data.csv contains the descriptive data of all participants (N=131).<br> The column labels are as follows:</p> <p>code: code of participant, p01-p131 <br> survey_period: survey period, summer_2021 / autumn_2020 / spring_2020<br> age: age of participant in years <br> grade: grade of participant <br> gen: gender, male (m) / female (f) / diverse (d) <br> ger_nat_speak: German native speaker, yes / no <br> repeater: Repeater of the last grade?, yes / no<br> math_course: basic course (basic) or advanced course (advanced) in mathematics<br> physics_course: basic course (basic) or advanced course (advanced) in physics<br> grade_math: last grade in mathematics, 1-6 <br> grade_phy: last grade mark in physics, 1-6 <br> grade_ger: last report mark in german, 1-6 <br> glasses: wearer of glasses?, yes / no<br> motivation: four-point lickert scale, very motivated (1) to not motivated at all (0) <br> test: physics tasks before math tasks (pm) or vice versa (mp) <br> For each test item 2 columns are created. Example Item k1:<br> ac_k1: answer correctness of item k1, not correct and/or guessed (0), correct (1). <br> cf_k1: confidence of item k1, four-point lickert scale, very confident (1) to guessed (0) </p>
ICSE'23: How Do We Read Formal Claims? Eye-Tracking and the Cognition of Proofs about Algorithms (Replication Materials)
<p>Formal methods are used successfully in high-assurance software, but they require rigorous mathematical and logical training that practitioners often lack. As such, integrating formal methods into software has been associated with numerous challenges. While educators have placed emphasis on formalisms in undergraduate theory courses, such courses often struggle with poor student outcomes and satisfaction. In this paper, we present a controlled eye-tracking human study (n=34) investigating the problem-solving strategies employed by students with different levels of incoming preparation (as assessed by theory coursework taken and pre-screening performance on a proof comprehension task), and how educators can better prepare low-outcome students for the rigorous logical reasoning that is a core part of formal methods in software engineering. We find that incoming preparation is not a good predictor of student outcomes for formalism comprehension tasks, and that student self-reports are not accurate at identifying factors associated with high outcomes for such tasks. Instead, and importantly, we find that differences in outcomes can be attributed to performance for proofs by induction and recursive algorithms, and that better-performing students exhibit significantly more attention switching behaviors, a result that has several implications for pedagogy in terms of the design of teaching materials. Our results suggest the need for a substantial pedagogical intervention in core theory courses to better align student outcomes with the objectives of mastery and retaining the material, and thus bettering preparing students for high-assurance software engineering.</p> <p>This artifact makes publicly available the de-identified eye-tracking and facial behavior analysis data that we collected in our controlled study of cognition of proofs about algorithms. We also include our Python scripts (as several Jupyter notebooks) used for the statistical analyses of the collected data. </p>
Eye-Control Trial: Wearable Eye-Tracking Device as Means of Communication
ClinicalTrials.gov study NCT04582149. IPD Sharing: YES. Countries: 1. Publications: 1.
Probing the Role of Feature Dimension Maps in Visual Cognition: Impact of Salience Level (Eye-tracking Follow-up Study)
ClinicalTrials.gov study NCT06852534. IPD Sharing: YES. Countries: 1. Publications: 14.
Eye-tracking data of translation evaluation
<p>Students and lecturers were asked to evaluate translations and select their favourite. On some of the slides, they were also provided with extrinsic information. </p> <p>Dataset includes project files, which need to be opened using GazePoint software. We have also included an Excel file which provides explanations on the participant background and selections. </p> <p> </p>
Benchmark Webcam Eye-Tracking Software
<p>These data correspond to the industrial systematic review and benchmark test set related to the paper <strong><em>A Benchmark Study of Webcam Eye Tracking Software in Robotic Process Automation</em>.</strong></p> <p>The industrial systematic review is an approach adapted from Kitchenham's method that gathers all the eye tracking tools obtained after running a search in search engines, entering defined queries, and filtering by inclusion/exclusion criteria that we define in our planning. More precisely, this review includes all the different results obtained from the search queries (<em>Eye tracking</em> or <em>Gaze tracking</em> <em>or Eye tracker</em>) and (<em>software</em> or <em>app</em> or <em>tool</em> or <em>github</em>) entered in the search engines <em>DuckDuckgo</em>, <em>Bing</em> and <em>Google</em>.</p> <p>After the search, the eye tracking software results will be filtered according to the exclusion/inclusion criteria (C):</p> <p>· C1: The software must be available and documented or supported.</p> <p>· C2: The software must work on desktop computers or laptops, excluding mobile devices.</p> <p>· C3: The software should be open-source and/or free software, and if it is not, a demo version should be available even if it is for a trial period.</p> <p>· C4: The software must provide the option to use the built-in/native camera of a PC or webcam as hardware for eye tracking. It is excluded head-mounted devices, Virtual Reality glasses and commercial eye tracker hardware.</p> <p>· C5: The software must provide Eye tracking as software, not as a feature or extension of a service unrelated about specifically predicting the POG as functionality.</p> <p>· C6: The software must be installable in the established benchmark test setup.</p> <p>The software that passes the inclusion/exclusion criteria are those evaluated in a benchmark.</p> <p> For this evaluation, there are 4 different objects where eye tracking software tests are performed. These objects are called <em>Circle</em>, <em>Buttons</em>, <em>Cartesian</em> <em>system</em> and <em>Email</em> <em>Invoice</em> and differ from each other in the elements and location of the targets for testing each eye tracking software.</p> <p>The results obtained from tests objects are strategically differentiated into two distinct categories to becnhmark eye tracking software according to Accuracy, Precision and POG Coincidence metrics:</p> <p>· <u>Accuracy and precision.</u> Measures directly the accuracy and precision of the eye tracking software in the <em>Cartesian system</em> object.</p> <p> o Accuracy. It is quantified as the angular error between the point representing the centroid of the estimated POG from the eye tracking software and the point representing the centroid of the target, at which the subject is gazing.</p> <p> o Precision. It is calculated using the root mean square (RMS) of the sampled points obtained from the screenshots of one test session.</p> <p>· <u>POG coincidence</u>. Classifies where the estimated POG is in each screenshot. This category applies to <em>Circle</em>, <em>Buttons</em> and <em>Invoice email</em> objects.</p> <p> o Match on target (green circle with a diameter of 1cm). The estimated POG centroid by the eye tracking software in the screenshot is inside the green circle area or touches its circumference or limit.</p> <p> o Match in a close target (blue circle with a radius of 2cm that contains the green circle (target) inscribed within it. The estimated POG centroid by the eye tracking software in the screenshot is inside the blue circle area or touches its circumference or limit.</p> <p> o Off-target. The estimated POG centroid by the eye tracking software in the screenshot is outside the target limit</p> <p> </p> <p>The structure of the data packages is organized as follows:</p> <p><strong>SLR_eye_tracking_software.xlsx: </strong>This .xlsx file corresponds entirely to the entire industrial systematic review on eye tracking software. It consists of 3 sheets:</p> <ul> <li><em>SLR</em>: This encapsulates the eye tracking software acquired through search queries inputted across diverse search engines. It is composed of 4 columns.</li> <ul> <li>SOFTWARE: Web platform or application that provides the eye tracking software.</li> <li>SOFTWARE URL: URL to the repository or host page of the software.</li> <li>SEARCH URL: URL of the page mentioning the software</li> <li>QUERY SEARCH: Keywords used for the search and entered in the search engines.</li> </ul> <li><em>Conducting</em>: This page shows the classification of eye tracking software according to the established inclusion/exclusion criteria, where each column corresponds to one of the criteria. Software that does not pass the successive criteria is discarded. Columns are as follows:</li> <ul> <li>AVAILABILITY; DOCUMENTATION AND SUPPORT (C1): Software that is downloadable and supported for viable use (Available, Not Available). Documentation and/or support about the software setup, run and outputs (Yes, No).</li> <li>PLATFORM (C2): Type of device and operating system where the sofware is run (PC: Windows, Ubuntu, MacOs, ...; Mobile: iOs, Android ...; Multiplatform).</li> <li>LICENSE (C3):</li> <ul> <li>OPENSOURCE: Public access to the source code (Yes, No).</li> <li>FREE SOFTWARE: It can be downloaded, used and distributed free of charge for non-commercial purposes (Yes, No).</li> <li>TRIAL DEMO: Availability of trial version if the software is neither open source nor free (Yes, No, -). *If it is opensource, trial demo value is “-“.</li> </ul> <li>HARDWARE (C4): Hardware required to run the software (Commercial, Webcam, Both, It is not determined).</li> <li>PURPOSE (C5): Eye tracking purposes or functionalities (Predict POG, AOI generation, Head motion capture, ...).</li> <li>BENCHMARK SETUP COMPATIBILITY (C6): The software shows compatibility with the benchmark setup (Yes, No).</li> </ul> <li><em>All Tools</em>: On this page, all the information obtained from each of the eye tracking software is displayed, without discarding based on the inclusion/exclusion criteria. In addition to the columns described on the conducting page, additional columns with extra information are included.</li> <ul> <li>SOFTWARE END-USE CONTEXT: Purpose of use of the software (Generic, Specific).</li> <li>SOURCE CODE: Eye tracker source code language (Java, C++, Python, JavaScript or it is not determined).</li> <li>INSTALLATION: Installation of the software in the system (Executable, SaaS, WebApp, Compiled, ...).</li> </ul> </ul> <p><strong>benchmark.xlsx: </strong>This file represents the results of the benchmark carried out on eye tracking software. It consists of two sheets.</p> <p> · <em>Accuracy + Precision</em>: It corresponds to the results obtained from the POG accuracy and precision tests conducted based on the Cartesian system object. Each column represents the following data:</p> <p> o <em>Software: </em>Eye tracking software tested.<em> </em></p> <p> o <em>Test ID: </em>Test identification.</p> <p> o <em>Screenshot ID: </em>Screenshot identification.</p> <p> o <em>Centroid POG X: </em>X-coordinate of the centroid of the (POG) estimated by the eye tracking software.</p> <p> o <em>Centroid POG Y: </em>Y-coordinate of the centroid of the (POG) estimated by the eye tracking software.</p> <p> o <em>Target X: </em>X-coordinate of the centroid of the target.</p> <p> o <em>Target Y: </em>Y-coordinate of the centroid of the target.</p> <p> o <em>Distance X (px): </em>Distance from the Centroid POG X to Target X (in pixels).</p> <p> o <em>Distance Y (px): </em>Distance from the Centroid POG Y to Target Y (in pixels).</p> <p> o <em>Euc.Distance (px): </em>Euclidean distance (in pixels) from the centroid of the POG to the centroid of the target point.</p> <p> o <em>Euc.Distance (cm): </em>Euclidean distance (in centimeters) from the centroid of the POG to the centroid of the target point.</p> <p> o <em>Average Euc.Distance (px): </em>Average Euclidean distance (in pixels) from the centroid of the Point of Gaze (POG) to the centroid of the target point, referred to the same test.</p> <p> o <em>Average euc.Distance (cm): </em>Average Euclidean distance (in centimeters) from the centroid of the Point of Gaze (POG) to the centroid of the origin point, referred to the same test.</p> <p> o <em>Precision: </em>Value of the Root Mean Square (RMS) of the average distance from the centroid of the Point of Gaze (POG) to the centroid of the target point.</p> <p> o <em><u>Accuracy (º</u></em><em>): Accuracy estimated (in degrees) according to the setup (60 centimeters and 0º eyes to screen target distance).</em></p> <p> o <em><u>Accuracy per points (º):</u></em><em> </em>Accuracy estimated (in degrees) per points referred to the same Test ID</p> <p> o <em><u>Average Accuracy per points (º):</u></em><em> </em>Average Accuracy (in degrees) per points referred to the same Test ID.</p> <p> · <em>Coincidence: </em>It corresponds to the results of the benchmark from the POG coincidence tests conducted based on Circle, Buttons and Invoice Email objects. Each column represents the following data:</p> <p> o Software: Eye tracking software tested.</p> <p> o Scenario: Object tested.</p> <p> o Test ID: Test identification.</p> <p> o Match Target: Number of Match target POG.</p> <p> o Close Target: Number of close target POG</p> <p> o Off Target: Number of Off target POG.</p> <p> o Match Target + Close Target: Sum of the match target POG and Close Target POG.</p> <p> o Total: Number of total POG: Total number of recounted points (50).</p> <p> o %Match Target: Percentage of match target POG.</p> <p> o %Close Target: Percentage of close target POG.</p> <p> o %Off Target: Percentage of off target POG.</p> <p> o %Match Target + Close Target: Sum of the match target POG and close target POG percentage.</p> <p><strong>benchmark_tests/</strong></p> <p> · <strong>tests_instructions.txt</strong></p> <p> It is an instruction manual containing information on how the tests are conducted in each object.</p> <p> · <strong>tests_scenarios/</strong></p> <p><strong> </strong>This folder contains the four objects designed for the benchmark evaluation.</p> <p> o <strong>circle.html</strong></p> <p> o <strong>buttons.html</strong></p> <p> o <strong>cartesian_system.html</strong></p> <p> o <strong>invoice_email.html</strong></p> <p> </p> <p> · <strong>eye-tracking_software_tests/</strong></p> <p> o <strong>GazeRecorder/</strong></p> <p> - <strong>buttons/</strong></p> <p> · <strong>T1/</strong></p> <p> o <strong>X_Y_Z.txt</strong></p> <p> o <strong>Recording_XXXXXXXXX_YYYY.mht</strong></p> <p> · <strong>T2/</strong></p> <p> o <strong>X_Y_Z.txt</strong></p> <p> o <strong>Recording_XXXXXXXXX_YYYY.mht</strong></p> <p> · <strong>T3/</strong></p> <p> o <strong>X_Y_Z.txt</strong></p> <p> o <strong>Recording_XXXXXXXXX_YYYY.mht</strong></p> <p> - <strong>circle/…</strong></p> <p> - <strong>invoice_email/…</strong></p> <p> - <strong>cartesian_system/</strong></p> <p> · <strong>T1/</strong></p> <p> o <strong>Recording_XXXXXXXXX_YYYY.mht</strong></p> <p> o <strong>1.png</strong></p> <p> o <strong>2.png</strong></p> <p> o <strong>…</strong></p> <p> o <strong>20.png</strong></p> <p> · <strong>T2/</strong></p> <p> o <strong>Recording_XXXXXXXXX_YYYY.mht</strong></p> <p> o <strong>1.png</strong></p> <p> o <strong>2.png</strong></p> <p> o <strong>…</strong></p> <p> o <strong>20.png</strong></p> <p> - <strong>(*) POG_GazeRecorder.png</strong></p> <p><strong> </strong></p> <p>The folder <em>eye tracking_software_tests</em> contains the results of the tests in a stratified manner. At the first level, there are folders with data belonging to the respective eye tracking software to be tested (GazeRecorder, Eyedid, and Webgazer.js). At the next level, corresponding to each software, there would be folders for the test objects (buttons, circle, cartesian_system, and invoice_email). Within each object test folder, we find folders corresponding to the Test ID. Within each Test ID, there are two files:</p> <p> · <strong>X_Y_Z.txt. </strong></p> <p> o X represents the number of match target POG estimated by the eye tracking software.</p> <p> o Y represents the number of of close target POG estimated by the eye tracking software.</p> <p> o Z represents the number of off-target POG estimated by the eye tracking software.</p> <p> · <strong>Recording_XXXXXXXXX_YYYY.mht. </strong></p> <p> o It is the User Interface log generated by the stepRecorder application for Windows. It contains 30 screenshots about the POG estimation for the coincidence tests objects (Buttons, Circle and Invoice Email) and 20 screenshots for the accuracy and precision test object (Cartesian System).</p> <p> · <strong>1.png to 20.png </strong></p> <p> o These screenshots are exported from the .mht file in order to obtain the centroid of the Point of Gaze (POG) and calculate accuracy and precision accurately, as it is not possible directly from the .mht file.</p> <p> </p> <p>(*) The Point of Gaze (POG) in GazeRecorder is not displayed as a small dot, as is common and as it happens in EyeDid and Webgazer.js, but rather as an eye encapsulated in a rectangle. POG_GazeRecorder.png shows the centroid for the POG estimated by that eye tracking software.</p>
Pointing movements and eye-tracking data_Facilitated Communication Users
<p>The repository contains all the pre-sorted data used for the analysis described in the paper. Data are divided into three:</p> <ul> <li>in A, we report the movement data of each pointing gesture considered in the analysis.</li> <li>in B we report keystrokes' related data.</li> <li>in C we report the sorted eye-tracking data.</li> </ul> <h3>A. User Correct Movements:</h3> <p>Each participant's data is organized into a 1xN cell array in Matlab, where N represents the number of pointing gestures analyzed. Each cell contains an Nx8 column vector with the following information:</p> <ol> <li> <p><strong>Time Information (column 1)</strong>:</p> <ul> <li>Time associated with the pointing gesture (milliseconds).</li> </ul> </li> <li> <p><strong>Arm Coordinates (columns 2,3 and 4)</strong>:</p> <ul> <li>X-axis coordinates (millimetres).</li> <li>Y-axis coordinates (millimetres).</li> <li>Z-axis coordinates (millimetres).</li> </ul> </li> <li> <p><strong>EMG Deltoid Activation (Facilitator) (columns 5, and 6) </strong>:</p> <ul> <li>Rectified EMG deltoid activation.</li> <li>Envelope EMG deltoid activation.</li> </ul> </li> <li> <p><strong>EMG Deltoid Activation (User) (columns 7 and 8)</strong>:</p> <ul> <li>Rectified EMG deltoid activation.</li> <li>Envelope EMG deltoid activation.</li> </ul> </li> </ol> <h3>B. Users' Keys Pressed with Probability:</h3> <p>Each participant's data is organized into an Nx6 string array, where N represents the number of pointing gestures analyzed. Each array contains the following information:</p> <ol> <li> <p><strong>Key Press Time</strong>:</p> <ul> <li>Absolute time the key is pressed (milliseconds, as recorded by the key-logger).</li> </ul> </li> <li> <p><strong>Time Between Keystrokes</strong>:</p> <ul> <li>Difference in milliseconds between two consecutive keystrokes.</li> </ul> </li> <li> <p><strong>Key Pressed</strong>:</p> <ul> <li>The key that has been pressed.</li> </ul> </li> <li> <p><strong>Pointing Time</strong>:</p> <ul> <li>Time taken by the arm to complete the forward phase of the pointing gesture (seconds).</li> </ul> </li> <li> <p><strong>Character Position</strong>:</p> <ul> <li>Position of the pressed character within the word (spacebar hits are assigned the number 300).</li> </ul> </li> <li> <p><strong>Key Selection Probability</strong>:</p> <ul> <li>Probability (percentage) of the key being selected.</li> </ul> </li> </ol> <h3><strong>C. EyeTracking data sorted</strong></h3> <p>Each participant's data is organized into an N×6 cell array, where N represents the number of pointing gestures analyzed through eye-tracking. The contents of each row are as follows:</p> <ol> <li> <p><strong>Fixation Data (Nx5 vector)</strong>:</p> <ul> <li><strong>N</strong> is the number of fixations related to one pointing gesture.</li> <li>Each vector contains: <ul> <li> <p><strong>The standardized time </strong>is determined by synchronizing the eye fixation with the arm movement. Given the movement duration is scaled from 0 to 10, we identify the moment when the eye-fixation occurs.</p> <p>This standardized time refers to the duration of the fixation relative to the duration of the pointing gesture. In <strong>column 1</strong>, we report the gross time, averaging the beginning and end of the fixation. In<strong> column 4</strong>, we provide the exact standardization at the start, and in <strong>column 5</strong>, the exact standardization at the end of the movement. During analysis, these times are synchronized with the movement duration, and we use the data from column 4.</p> </li> <li> <p><strong>Euclidean distance</strong> between the fixated key and the target key (2nd column).</p> </li> <li><strong>Duration</strong> of each fixation (3rd column).</li> </ul> </li> </ul> </li> <li> <p><strong>Sequence of Fixated Keys</strong>:</p> <ul> <li>Contains the sequence of keys fixated by the user during each pointing gesture.</li> </ul> </li> <li> <p><strong>Arm Movement Information</strong>:</p> <ul> <li>Includes details on the arm movement (same as reported in<strong> file A</strong>) corresponding to the eye-tracking fixation sequence.</li> </ul> </li> <li> <p><strong>Target Key Pressed</strong>:</p> <ul> <li>Indicates the target key pressed by the participant.</li> </ul> </li> <li> <p><strong>Probability of Key Pressed</strong>:</p> <ul> <li>Reports the likelihood of each key being pressed,<strong> as detailed in file B.</strong></li> </ul> </li> <li> <p><strong>Euclidean Distance Between Consecutive Keys</strong>:</p> <ul> <li>Measures the Euclidean distance between two consecutively pressed keys using the keyboard as a reference (refer to the paper text for more details).</li> </ul> </li> </ol> <p> </p> <p> </p>
Dataset for BPM2024, Educators Forum: Comprehension of (business) process models via tokens: an eye-tracking approach
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