Application of hidden Markov models to eye tracking data analysis of visual quality inspection operations

Ulutas, Berna Haktanirlar; Ozkan, Necmettin; Michalski, Rafał

doi:10.1007/s10100-019-00628-x

Cited by 31 publications

(15 citation statements)

References 32 publications

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“…In Rajakumari et al [86], the authors recognized six basic emotions in their works namely anger, fear, happiness, focus, sleep, and disgust by using a Hidden Markov Model (HMM), which is widely used machine learning approach [87][88][89][90][91]. The study of Ulutas et al [92] and Chuk et al [93] presented the applications of HMM to eye-tracking data. They carried out the studies by measuring the distance between sclera and iris which were then used as features to classify the above mentioned six emotions.…”

Section: Distance Between Sclera and Irismentioning

confidence: 99%

Emotion Recognition Using Eye-Tracking: Taxonomy, Review and Current Challenges

Lim

Mountstephens

Teo

2020

Sensors

180

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The ability to detect users’ emotions for the purpose of emotion engineering is currently one of the main endeavors of machine learning in affective computing. Among the more common approaches to emotion detection are methods that rely on electroencephalography (EEG), facial image processing and speech inflections. Although eye-tracking is fast in becoming one of the most commonly used sensor modalities in affective computing, it is still a relatively new approach for emotion detection, especially when it is used exclusively. In this survey paper, we present a review on emotion recognition using eye-tracking technology, including a brief introductory background on emotion modeling, eye-tracking devices and approaches, emotion stimulation methods, the emotional-relevant features extractable from eye-tracking data, and most importantly, a categorical summary and taxonomy of the current literature which relates to emotion recognition using eye-tracking. This review concludes with a discussion on the current open research problems and prospective future research directions that will be beneficial for expanding the body of knowledge in emotion detection using eye-tracking as the primary sensor modality.

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Section: Distance Between Sclera and Irismentioning

confidence: 99%

Emotion Recognition Using Eye-Tracking: Taxonomy, Review and Current Challenges

Lim

Mountstephens

Teo

2020

Sensors

180

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“…Recent research demonstrated that HMM-based approaches may unravel new insights about cognitive functions (e.g., learning, decision making) [ 58 , 59 ]. A HMM allows for the identification of not yet considered commonalities as well as differences between novices and experts during the comprehension of process models, thereby enabling a better support in model comprehension [ 60 ]. All potential findings collected during the three studies can be used to foster process model comprehension, thereby having direct impacts on research and practice (see Figure 6 ).…”

Section: Discussionmentioning

confidence: 99%

Applying Eye Movement Modeling Examples to Guide Novices’ Attention in the Comprehension of Process Models

et al. 2021

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Process models are crucial artifacts in many domains, and hence, their proper comprehension is of importance. Process models mediate a plethora of aspects that are needed to be comprehended correctly. Novices especially face difficulties in the comprehension of process models, since the correct comprehension of such models requires process modeling expertise and visual observation capabilities to interpret these models correctly. Research from other domains demonstrated that the visual observation capabilities of experts can be conveyed to novices. In order to evaluate the latter in the context of process model comprehension, this paper presents the results from ongoing research, in which gaze data from experts are used as Eye Movement Modeling Examples (EMMEs) to convey visual observation capabilities to novices. Compared to prior results, the application of EMMEs improves process model comprehension significantly for novices. Novices achieved in some cases similar performances in process model comprehension to experts. The study’s insights highlight the positive effect of EMMEs on fostering the comprehension of process models.

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“…Their dynamics may be modeled by hidden Markov models (cf. [102][103][104][105][106][107][108]), their fuzzy equivalents (see [109][110][111]), or even Markov switching models (cf. [112,113]).…”

Section: Validation Studiesmentioning

confidence: 99%

Modeling human thinking about similarities by neuromatrices in the perspective of fuzzy logic

Grobelny

Michalski

Weber

2020

Neural Comput & Applic

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In this work, we propose a new method for modeling human reasoning about objects’ similarities. We assume that similarity depends on perceived intensities of objects’ attributes expressed by natural language expressions such as low, medium, and high. We show how to find the underlying structure of the matrix with intensities of objects’ similarities in the factor-analysis-like manner. The demonstrated approach is based on fuzzy logic and set theory principles, and it uses only maximum and minimum operators. Similarly to classic eigenvector decomposition, we aim at representing the initial linguistic ordinal-scale (LOS) matrix as a max–min product of other LOS matrix and its transpose. We call this reconstructing matrix a neuromatrix because we assume that such a process takes place at the neural level in our brain. We show and discuss on simple, illustrative examples, how the presented way of modeling grasps natural way of reasoning about similarities. The unique characteristics of our approach are treating smaller attribute intensities as less important in making decisions about similarities. This feature is consistent with how the human brain is functioning at a biological level. A neuron fires and passes information further only if input signals are strong enough. The proposal of the heuristic algorithm for finding the decomposition in practice is also introduced and applied to exemplary data from classic psychological studies on perceived similarities between colors and between nations. Finally, we perform a series of simulation experiments showing the effectiveness of the proposed heuristic.

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Application of hidden Markov models to eye tracking data analysis of visual quality inspection operations

Cited by 31 publications

References 32 publications

Emotion Recognition Using Eye-Tracking: Taxonomy, Review and Current Challenges

Emotion Recognition Using Eye-Tracking: Taxonomy, Review and Current Challenges

Applying Eye Movement Modeling Examples to Guide Novices’ Attention in the Comprehension of Process Models

Modeling human thinking about similarities by neuromatrices in the perspective of fuzzy logic

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