VisME: Visual microsaccades explorer

Munz, Tanja; Chuang, Lewis L.; Pannasch, Sebastian; Weiskopf, Daniel

doi:10.16910/jemr.12.6.5

Cited by 3 publications

(2 citation statements)

References 48 publications

(72 reference statements)

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“… Eye tracking algorithms: ( a ) graphical overview of gaze tracking and mapping, overlapping data on user’s eye view (Reproduced under terms of the CC-BY license [ 167 ]. Copyright 2020, the Authors.…”

Section: Eye Trackersmentioning

confidence: 99%

Advances in Materials, Sensors, and Integrated Systems for Monitoring Eye Movements

et al. 2022

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Eye movements show primary responses that reflect humans' voluntary intention and conscious selection. Because visual perception is one of the fundamental sensory interactions in the brain, eye movements contain critical information regarding physical/psychological health, perception, intention, and preference. With the advancement of wearable device technologies, the performance of monitoring eye tracking has been significantly improved. It also has led to myriad applications for assisting and augmenting human activities. Among them, electrooculograms, measured by skin-mounted electrodes, have been widely used to track eye motions accurately. In addition, eye trackers that detect reflected optical signals offer alternative ways without using wearable sensors. This paper outlines a systematic summary of the latest research on various materials, sensors, and integrated systems for monitoring eye movements and enabling human-machine interfaces. Specifically, we summarize recent developments in soft materials, biocompatible materials, manufacturing methods, sensor functions, systems' performances, and their applications in eye tracking. Finally, we discuss the remaining challenges and suggest research directions for future studies.

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Section: Eye Trackersmentioning

confidence: 99%

Advances in Materials, Sensors, and Integrated Systems for Monitoring Eye Movements

et al. 2022

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“…Therefore, it might become relevant to even provide visual analysis tools and descriptions thereof along with the research data. Alternatively, our community could establish a set of tools on which the reproducibility of studies could rely, adopting similar ideas from eye tracking research [61]. The issues of both storytelling and open science are connected to the development of visual data analysis methods in the sense of Vis4Vis.…”

Section: New Ways Of Reporting Privacy Preservation and Open Science!mentioning

confidence: 99%

Vis4Vis: Visualization for (Empirical) Visualization Research

Weiskopf¹

2019

Preprint

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Appropriate evaluation is a key component in visualization research. It is typically based on empirical studies that assess visualization components or complete systems. While such studies often include the user of the visualization, empirical research is not necessarily restricted to user studies but may also address the technical performance of a visualization system such as its computational speed or memory consumption. Any such empirical experiment faces the issue that the underlying visualization is becoming increasingly sophisticated, leading to an increasingly difficult evaluation in complex environments. Therefore, many of the established methods of empirical studies can no longer capture the full complexity of the evaluation. One promising solution is the use of data-rich observations that we can acquire during studies to obtain more reliable interpretations of empirical research. For example, we have been witnessing an increasing availability and use of physiological sensor information from eye tracking, electrodermal activity sensors, electroencephalography, etc. Other examples are various kinds of logs of user activities such as mouse, keyboard, or touch interaction. Such data-rich empirical studies promise to be especially useful for studies in the wild and similar scenarios outside of the controlled laboratory environment. However, with the growing availability of large, complex, time-dependent, heterogeneous, and unstructured observational data, we are facing the new challenge of how we can analyze such data. This challenge can be addressed by establishing the subfield of visualization for visualization (Vis4Vis): visualization as a means of analyzing and communicating data from empirical studies to advance visualization research.

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Vis4Vis: Visualization for (Empirical) Visualization Research

Weiskopf

2020

Foundations of Data Visualization

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This paper revisits the role of quantitative and qualitative methods in visualization research in the context of advancements in artificial intelligence (AI). The focus is on how we can bridge between the different methods in an integrated process of analyzing user study data. To this end, a process model of-potentially iteratedsemantic enrichment and transformation of data is proposed. This joint perspective of data and semantics facilitates the integration of quantitative and qualitative methods. The model is motivated by examples of own prior work, especially in the area of eye tracking user studies and coding data-rich observations. Finally, there is a discussion of open issues and research opportunities in the interplay between AI, human analyst, and qualitative and quantitative methods for visualization research.

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VisME: Visual microsaccades explorer

Cited by 3 publications

References 48 publications

Advances in Materials, Sensors, and Integrated Systems for Monitoring Eye Movements

Advances in Materials, Sensors, and Integrated Systems for Monitoring Eye Movements

Vis4Vis: Visualization for (Empirical) Visualization Research

Vis4Vis: Visualization for (Empirical) Visualization Research

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