scholarly journals Development of Open-source Software and Gaze Data Repositories for Performance Evaluation of Eye Tracking Systems

Vision ◽  
2019 ◽  
Vol 3 (4) ◽  
pp. 55
Author(s):  
Kar ◽  
Corcoran
Keyword(s):  
Eye Tracking ◽  
Data Quality ◽  
Open Source ◽  
Eye Gaze ◽  
Operating Conditions ◽  
Data Repository ◽  
Tracking Systems ◽  
Data Repositories ◽  
Multiple User ◽  
The Impact

In this paper, a range of open-source tools, datasets, and software that have been developed for quantitative and in-depth evaluation of eye gaze data quality are presented. Eye tracking systems in contemporary vision research and applications face major challenges due to variable operating conditions such as user distance, head pose, and movements of the eye tracker platform. However, there is a lack of open-source tools and datasets that could be used for quantitatively evaluating an eye tracker’s data quality, comparing performance of multiple trackers, or studying the impact of various operating conditions on a tracker’s accuracy. To address these issues, an open-source code repository named GazeVisual-Lib is developed that contains a number of algorithms, visualizations, and software tools for detailed and quantitative analysis of an eye tracker’s performance and data quality. In addition, a new labelled eye gaze dataset that is collected from multiple user platforms and operating conditions is presented in an open data repository for benchmark comparison of gaze data from different eye tracking systems. The paper presents the concept, development, and organization of these two repositories that are envisioned to improve the performance analysis and reliability of eye tracking systems.

scholarly journals MLGaze: Machine Learning-Based Analysis of Gaze Error Patterns in Consumer Eye Tracking Systems

Vision ◽  
2020 ◽  
Vol 4 (2) ◽  
pp. 25
Author(s):  
Anuradha Kar
Keyword(s):  
Machine Learning ◽  
Eye Tracking ◽  
Data Quality ◽  
Regression Models ◽  
Operating Conditions ◽  
Eye Tracker ◽  
Error Sources ◽  
Error Patterns ◽  
Machine Learning Methods ◽  
The Impact

Analyzing the gaze accuracy characteristics of an eye tracker is a critical task as its gaze data is frequently affected by non-ideal operating conditions in various consumer eye tracking applications. In previous research on pattern analysis of gaze data, efforts were made to model human visual behaviors and cognitive processes. What remains relatively unexplored are questions related to identifying gaze error sources as well as quantifying and modeling their impacts on the data quality of eye trackers. In this study, gaze error patterns produced by a commercial eye tracking device were studied with the help of machine learning algorithms, such as classifiers and regression models. Gaze data were collected from a group of participants under multiple conditions that commonly affect eye trackers operating on desktop and handheld platforms. These conditions (referred here as error sources) include user distance, head pose, and eye-tracker pose variations, and the collected gaze data were used to train the classifier and regression models. It was seen that while the impact of the different error sources on gaze data characteristics were nearly impossible to distinguish by visual inspection or from data statistics, machine learning models were successful in identifying the impact of the different error sources and predicting the variability in gaze error levels due to these conditions. The objective of this study was to investigate the efficacy of machine learning methods towards the detection and prediction of gaze error patterns, which would enable an in-depth understanding of the data quality and reliability of eye trackers under unconstrained operating conditions. Coding resources for all the machine learning methods adopted in this study were included in an open repository named MLGaze to allow researchers to replicate the principles presented here using data from their own eye trackers.

scholarly journals ScholarLens: extracting competences from research publications for the automatic generation of semantic user profiles

2017 ◽  
Vol 3 ◽  
pp. e121 ◽  
Author(s):  
Bahar Sateli ◽  
Felicitas Löffler ◽  
Birgitta König-Ries ◽  
René Witte
Keyword(s):  
Open Source ◽  
Open Data ◽  
Linked Open Data ◽  
User Profiling ◽  
Research Articles ◽  
User Profiles ◽  
Data Repositories ◽  
Link Type ◽  
Research Objects ◽  
The Impact

Motivation Scientists increasingly rely on intelligent information systems to help them in their daily tasks, in particular for managing research objects, like publications or datasets. The relatively young research field of Semantic Publishing has been addressing the question how scientific applications can be improved through semantically rich representations of research objects, in order to facilitate their discovery and re-use. To complement the efforts in this area, we propose an automatic workflow to construct semantic user profiles of scholars, so that scholarly applications, like digital libraries or data repositories, can better understand their users’ interests, tasks, and competences, by incorporating these user profiles in their design. To make the user profiles sharable across applications, we propose to build them based on standard semantic web technologies, in particular the Resource Description Framework (RDF) for representing user profiles and Linked Open Data (LOD) sources for representing competence topics. To avoid the cold start problem, we suggest to automatically populate these profiles by analyzing the publications (co-)authored by users, which we hypothesize reflect their research competences. Results We developed a novel approach, ScholarLens, which can automatically generate semantic user profiles for authors of scholarly literature. For modeling the competences of scholarly users and groups, we surveyed a number of existing linked open data vocabularies. In accordance with the LOD best practices, we propose an RDF Schema (RDFS) based model for competence records that reuses existing vocabularies where appropriate. To automate the creation of semantic user profiles, we developed a complete, automated workflow that can generate semantic user profiles by analyzing full-text research articles through various natural language processing (NLP) techniques. In our method, we start by processing a set of research articles for a given user. Competences are derived by text mining the articles, including syntactic, semantic, and LOD entity linking steps. We then populate a knowledge base in RDF format with user profiles containing the extracted competences.We implemented our approach as an open source library and evaluated our system through two user studies, resulting in mean average precision (MAP) of up to 95%. As part of the evaluation, we also analyze the impact of semantic zoning of research articles on the accuracy of the resulting profiles. Finally, we demonstrate how these semantic user profiles can be applied in a number of use cases, including article ranking for personalized search and finding scientists competent in a topic —e.g., to find reviewers for a paper. Availability All software and datasets presented in this paper are available under open source licenses in the supplements and documented at http://www.semanticsoftware.info/semantic-user-profiling-peerj-2016-supplements. Additionally, development releases of ScholarLens are available on our GitHub page: https://github.com/SemanticSoftwareLab/ScholarLens.

scholarly journals An Open Conversation on Using Eye-Gaze Methods in Studies of Neurodevelopmental Disorders

2015 ◽  
Vol 58 (6) ◽  
pp. 1719-1732 ◽  
Author(s):  
Courtney E. Venker ◽  
Sara T. Kover
Keyword(s):  
Missing Data ◽  
Eye Tracking ◽  
Neurodevelopmental Disorders ◽  
Eye Gaze ◽  
Specific Information ◽  
Cascading Effects ◽  
Open Discussion ◽  
The Impact ◽  
Implementing Strategies ◽  
Selection Of

Purpose Eye-gaze methods have the potential to advance the study of neurodevelopmental disorders. Despite their increasing use, challenges arise in using these methods with individuals with neurodevelopmental disorders and in reporting sufficient methodological detail such that the resulting research is replicable and interpretable. Method This tutorial presents key considerations involved in designing and conducting eye-gaze studies for individuals with neurodevelopmental disorders and proposes conventions for reporting the results of such studies. Results Methodological decisions (e.g., whether to use automated eye tracking or manual coding, implementing strategies to scaffold children's performance, defining valid trials) have cascading effects on the conclusions drawn from eye-gaze data. Research reports that include specific information about procedures, missing data, and selection of participants will facilitate interpretation and replication. Conclusions Eye-gaze methods provide exciting opportunities for studying neurodevelopmental disorders. Open discussion of the issues presented in this tutorial will improve the pace of productivity and the impact of advances in research on neurodevelopmental disorders.

scholarly journals The stability of the MEMS 2D mirror's operating point in terms of eye tracking systems

2020 ◽  
Vol 12 (2) ◽  
pp. 43
Author(s):  
Mateusz Pomianek ◽  
Marek Piszczek ◽  
Marcin Maciejewski ◽  
Piotr Krukowski
Keyword(s):  
Eye Tracking ◽  
Laser Scanning ◽  
Tracking System ◽  
Eye Gaze ◽  
Main Element ◽  
Head Tracking ◽  
Tracking Systems ◽  
Gaze Tracking ◽  
Pupil Detection ◽  
The Stability

This paper describes research on the stability of the MEMS mirror for use in eye tracking systems. MEMS mirrors are the main element in scanning methods (which is one of the methods of eye tracking). Due to changes in the mirror pitch, the system can scan the area of the eye with a laser and collect the signal reflected. However, this method works on the assumption that the inclinations are constant in each period. The instability of this causes errors. The aim of this work is to examine the error level caused by pitch instability at different points of work. Full Text: PDF ReferencesW. Fuhl, M. Tonsen, A. Bulling, and E. Kasneci, "Pupil detection for head-mounted eye tracking in the wild: an evaluation of the state of the art," Mach. Vis. Appl., vol. 27, no. 8, pp. 1275-1288, 2016, CrossRef X. Wang, S. Koch, K. Holmqvist, and M. Alexa, "Tracking the gaze on objects in 3D," ACM Trans. Graph., vol. 37, no. 6, pp. 1-18, Dec. 2018 CrossRef X. Xiong and H. Xie, "MEMS dual-mode electrostatically actuated micromirror," Proc. 2014 Zo. 1 Conf. Am. Soc. Eng. Educ. - "Engineering Educ. Ind. Involv. Interdiscip. Trends", ASEE Zo. 1 2014, no. Dmd, 2014 CrossRef E. Pengwang, K. Rabenorosoa, M. Rakotondrabe, and N. Andreff, "Scanning micromirror platform based on MEMS technology for medical application," Micromachines, vol. 7, no. 2, 2016 CrossRef J. P. Giannini, A. G. York, and H. Shroff, "Anticipating, measuring, and minimizing MEMS mirror scan error to improve laser scanning microscopy's speed and accuracy," PLoS One, vol. 12, no. 10, pp. 1-14, 2017 CrossRef C. Hennessey, B. Noureddin, and P. Lawrence, "A single camera eye-gaze tracking system with free head motion," Eye Track. Res. Appl. Symp., vol. 2005, no. March, pp. 87-94, 2005 CrossRef C. H. Morimoto and M. R. M. Mimica, "Eye gaze tracking techniques for interactive applications," Comput. Vis. Image Underst., vol. 98, no. 1, pp. 4-24, Apr. 2005 CrossRef S. T. S. Holmström, U. Baran, and H. Urey, "MEMS laser scanners: A review," J. Microelectromechanical Syst., vol. 23, no. 2, pp. 259-275, 2014 CrossRef C. W. Cho, "Gaze Detection by Wearable Eye-Tracking and NIR LED-Based Head-Tracking Device Based on SVR," ETRI J., vol. 34, no. 4, pp. 542-552, Aug. 2012 CrossRef T. Santini, W. Fuhl, and E. Kasneci, "PuRe: Robust pupil detection for real-time pervasive eye tracking," Comput. Vis. Image Underst., vol. 170, pp. 40-50, May 2018 CrossRef O. Solgaard, A. A. Godil, R. T. Howe, L. P. Lee, Y. A. Peter, and H. Zappe, "Optical MEMS: From micromirrors to complex systems," J. Microelectromechanical Syst., vol. 23, no. 3, pp. 517-538, 2014 CrossRef J. Wang, G. Zhang, and Z. You, "UKF-based MEMS micromirror angle estimation for LiDAR," J. Micromechanics Microengineering, vol. 29, no. 3, 201 CrossRef

scholarly journals Exploring completeness in clinical data research networks with DQe-c

2017 ◽  
Vol 25 (1) ◽  
pp. 17-24 ◽  
Author(s):  
Hossein Estiri ◽  
Kari A Stephens ◽  
Jeffrey G Klann ◽  
Shawn N Murphy
Keyword(s):  
Data Quality ◽  
Quality Assessment ◽  
Clinical Data ◽  
Data Science ◽  
Ad Hoc ◽  
Assessment Tool ◽  
Data Repository ◽  
Process Data ◽  
Data Repositories ◽  
Data Quality Assessment

Abstract Objective To provide an open source, interoperable, and scalable data quality assessment tool for evaluation and visualization of completeness and conformance in electronic health record (EHR) data repositories. Materials and Methods This article describes the tool’s design and architecture and gives an overview of its outputs using a sample dataset of 200 000 randomly selected patient records with an encounter since January 1, 2010, extracted from the Research Patient Data Registry (RPDR) at Partners HealthCare. All the code and instructions to run the tool and interpret its results are provided in the Supplementary Appendix. Results DQe-c produces a web-based report that summarizes data completeness and conformance in a given EHR data repository through descriptive graphics and tables. Results from running the tool on the sample RPDR data are organized into 4 sections: load and test details, completeness test, data model conformance test, and test of missingness in key clinical indicators. Discussion Open science, interoperability across major clinical informatics platforms, and scalability to large databases are key design considerations for DQe-c. Iterative implementation of the tool across different institutions directed us to improve the scalability and interoperability of the tool and find ways to facilitate local setup. Conclusion EHR data quality assessment has been hampered by implementation of ad hoc processes. The architecture and implementation of DQe-c offer valuable insights for developing reproducible and scalable data science tools to assess, manage, and process data in clinical data repositories.

scholarly journals Street Network Models and Indicators for Every Urban Area in the World

2020 ◽  
Author(s):  
Geoff Boeing
Keyword(s):  
Open Source ◽  
Urban Area ◽  
Open Data ◽  
Network Models ◽  
Data Repository ◽  
Street Network ◽  
Data Repositories ◽  
Urban Street ◽  
The World ◽  
Network Form

Cities worldwide exhibit a variety of street network patterns and configurations that shape human mobility, equity, health, and livelihoods. This study models and analyzes the street networks of each urban area in the world, using boundaries derived from the Global Human Settlement Layer. Street network data are acquired and modeled from OpenStreetMap with the open-source OSMnx software. In total, this study models over 160 million OpenStreetMap street network nodes and over 320 million edges across 8,914 urban areas in 178 countries, and attaches elevation and grade data. This article presents the study's reproducible computational workflow, introduces two new open data repositories of ready-to-use global street network models and calculated indicators, and discusses summary findings on street network form worldwide. It makes four contributions. First, it reports the methodological advances of this open-source workflow. Second, it produces an open data repository containing street network models for each urban area. Third, it analyzes these models to produce an open data repository containing street network form indicators for each urban area. No such global urban street network indicator dataset has previously existed. Fourth, it presents a summary analysis of urban street network form, reporting the first such worldwide results in the literature.

scholarly journals Is the quality of hospital EHR data sufficient to evidence its ICHOM outcomes performance in heart failure? A pilot evaluation

2021 ◽  
Author(s):  
Hannelore Aerts ◽  
Dipak Kalra ◽  
Carlos Saez ◽  
Juan Manuel Ramírez-Anguita ◽  
Miguel-Angel Mayer ◽  
...  
Keyword(s):  
Heart Failure ◽  
Pilot Study ◽  
Health Outcomes ◽  
Data Quality ◽  
Quality Assessment ◽  
Temporal Stability ◽  
Data Availability ◽  
Future Research ◽  
Data Repository ◽  
The Impact

AbstractThere is increasing recognition that healthcare providers need to focus attention, and be judged against, the impact they have on the health outcomes experienced by patients. The measurement of health outcomes as a routine part of clinical documentation is probably the only scalable way of collecting outcomes evidence, since secondary data collection is expensive and error prone. However, there is uncertainty about whether routinely collected clinical data within EHR systems includes the data most relevant to measuring and comparing outcomes, and if those items are collected to a good enough data quality to be relied upon for outcomes assessment, since several studies have pointed out significant issues regarding EHR data availability and quality.In this paper, we first describe a practical approach to data quality assessment of health outcomes, based on a literature review of existing frameworks for quality assessment of health data and multi-stakeholder consultation. Adopting this approach, we perform a pilot study on a subset of 21 International Consortium for Health Outcomes Measurement (ICHOM) outcomes data items from patients with congestive heart failure. To this end, all available registries compatible with the diagnosis of heart failure within the IMASIS-2 data repository connected to the Hospital del Mar network (142,345 visits of 12,503 patients) were extracted and mapped to the ICHOM format. We focus our pilot assessment on five commonly used data quality dimensions: completeness, correctness, consistency, uniqueness and temporal stability.Overall, this pilot study reveals high scores on the consistency, completeness and uniqueness dimensions. Temporal stability analyses show some changes over time in the reported use of medication to treat heart failure, as well as in the recording of past medical conditions. Finally, investigation of data correctness suggests several issues concerning the proper characterization of missing data values. Many of these issues appear to be introduced while mapping the IMASIS-2 relational database contents to the ICHOM format, as the latter requires a level of detail which is not explicitly available in the coded data of an EHR.To truly examine to what extent hospitals today are able to routinely collect the evidence of their success in achieving good health outcomes, future research would benefit from performing more extensive data quality assessments, including all data items from the ICHOM heart failure standard set, across multiple hospitals.

scholarly journals Frictionless Data: Making Research Data Quality Visible

2018 ◽  
Vol 12 (2) ◽  
pp. 274-285 ◽  
Author(s):  
Dan Fowler ◽  
Jo Barratt ◽  
Paul Walsh
Keyword(s):  
Data Analysis ◽  
Best Practices ◽  
Data Quality ◽  
Open Source ◽  
Open Source Software ◽  
Research Data ◽  
Data Preparation ◽  
Data Repositories ◽  
Preparation Techniques ◽  
Ongoing Project

There is significant friction in the acquisition, sharing, and reuse of research data. It is estimated that eighty percent of data analysis is invested in the cleaning and mapping of data (Dasu and Johnson,2003). This friction hampers researchers not well versed in data preparation techniques from reusing an ever-increasing amount of data available within research data repositories. Frictionless Data is an ongoing project at Open Knowledge International focused on removing this friction. We are doing this by developing a set of tools, specifications, and best practices for describing, publishing, and validating data. The heart of this project is the “Data Package”, a containerization format for data based on existing practices for publishing open source software. This paper will report on current progress toward that goal.

scholarly journals Exploring the Potential of Online Webcam-based Eye Tracking in Decision-Making Research and Influence Factors on Data Quality

2021 ◽  
Author(s):  
Tim Schneegans ◽  
Matthew D. Bachman ◽  
Scott A. Huettel ◽  
Hauke Heekeren
Keyword(s):  
Decision Making ◽  
Eye Tracking ◽  
Data Quality ◽  
Open Source ◽  
Influence Factors ◽  
Frame Rate ◽  
Quality Data ◽  
Tracking Data ◽  
Chin Rest ◽  
Negative Effect

Recent developments of open-source online eye-tracking algorithms suggests that they may be ready for use in online studies, thereby overcoming the limitations of in-lab eye-tracking studies. However, to date there have been limited tests of the efficacy of online eye-tracking for decision-making and cognitive psychology. In this online study, we explore the potential and the limitations of online eye-tracking tools for decision-making research using the webcam-based open-source library Webgazer (Papoutsaki et al., 2016). Our study had two aims. For our first aim we assessed different variables that might affect the quality of eye-tracking data. In our experiment (N = 210) we measured a within-subjects variable of adding a provisional chin rest and a between-subjects variable of corrected vs uncorrected vision. Contrary to our hypotheses, we found that the chin rest had a negative effect on data quality. In accordance with out hypotheses, we found lower quality data in participants who wore glasses. Other influence factors are discussed, such as the frame rate. For our second aim (N = 44) we attempted to replicate a decision-making paradigm where eye-tracking data was acquired using offline means (Amasino et al., 2019). We found some relations between choice behavior and eye-tracking measures, such as the last fixation and the distribution of gaze points at the moment right before the choice. However, several effects could not be reproduced, such as the overall distribution of gaze points or dynamic search strategies. Therefore, our hypotheses only find partial evidence. This study gives practical insights for the feasibility of online eye-tacking for decision making research as well as researchers from other disciplines.

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