COLET: A dataset for COgnitive workLoad estimation based on eye-tracking

Ktistakis, Emmanouil; Skaramagkas, Vasileios; Manousos, Dimitris; Tachos, Nikolaos S.; Tripoliti, Evanthia E.; Fotiadis, Dimitrios I.; Tsiknakis, Manolis

doi:10.1016/j.cmpb.2022.106989

Cited by 16 publications

(10 citation statements)

References 38 publications

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“…Next, we will describe in more detail the few studies using deep learning. Rello et al [12] SVM Dyslexia 1135 (97) Benfatto et al [5] SVM Dyslexia 185 (185) Smymakis et al [11] Bayesian Dyslexia 66 (66) Asvestopoulou et al [6] Multiple Dyslexia 66 (66) Prabha et al [7] SVM Dyslexia 185 (185) Bixler et al [13] Multiple Mind wondering 4977 (178) Skaramagkas et al [14] MLP Predicting emotional State -(48) Jothiprabha et al [15] k-mean Dyslexia severity 97 (97) Rizzo et al [16] Multiple Detecting Cognitive Interference 64 (64) Ktistakis et al [17] Multiple Congitive workload estimation 47 (47) Vajs et al [18] Multiple Dyslexia 378 (30) Stephen et al [19] Bayesian Mind wondering 384 (32) Networks…”

Section: Related Work 21 Traditional Machine Learning Methodsmentioning

confidence: 99%

Deep Learning-Based Detection of Learning Disorders on a Large Scale Dataset of Eye Movement Records

El Hmimdi,

Kapoula,

Sainte Fare Garnot

2024

BioMedInformatics

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Early detection of dyslexia and learning disorders is vital for avoiding a learning disability, as well as supporting dyslexic students by tailoring academic programs to their needs. Several studies have investigated using supervised algorithms to screen dyslexia vs control subjects; however, the data size and the conditions of data acquisition were their most significant limitation. In the current study, we leverage a large dataset, containing 4243 time series of eye movement records from children across Europe. These datasets were derived from various tests such as saccade, vergence, and reading tasks. Furthermore, our methods were evaluated with realistic test data, including real-life biases such as noise, eye tracking misalignment, and similar pathologies among non-scholar difficulty classes. In addition, we present a novel convolutional neural network architecture, adapted to our time series classification problem, that is intended to generalize on a small annotated dataset and to handle a high-resolution signal (1024 point). Our architecture achieved a precision of 80.20% and a recall of 75.1%, when trained on the vergence dataset, and a precision of 77.2% and a recall of 77.5% when trained on the saccade dataset. Finally, we performed a comparison using our ML approach, a second architecture developed for a similar problem, and two other methods that we investigated that use deep learning algorithms to predict dyslexia.

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Section: Related Work 21 Traditional Machine Learning Methodsmentioning

confidence: 99%

Deep Learning-Based Detection of Learning Disorders on a Large Scale Dataset of Eye Movement Records

El Hmimdi,

Kapoula,

Sainte Fare Garnot

2024

BioMedInformatics

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“…This study analyzed eye movements from 47 individuals engaged in tasks of varying complexity and employed machine learning for predicting workload levels. It revealed notable impacts of multitasking and time pressure on eye movement characteristics and achieved an accuracy of up to 88% in estimating workload levels [172].…”

Section: Machine Learning and Aimentioning

confidence: 99%

Examining the Landscape of Cognitive Fatigue Detection: A Comprehensive Survey

Karim,

Pavel,

Nikanfar

et al. 2024

Technologies

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Cognitive fatigue, a state of reduced mental capacity arising from prolonged cognitive activity, poses significant challenges in various domains, from road safety to workplace productivity. Accurately detecting and mitigating cognitive fatigue is crucial for ensuring optimal performance and minimizing potential risks. This paper presents a comprehensive survey of the current landscape in cognitive fatigue detection. We systematically review various approaches, encompassing physiological, behavioral, and performance-based measures, for robust and objective fatigue detection. The paper further analyzes different challenges, including the lack of standardized ground truth and the need for context-aware fatigue assessment. This survey aims to serve as a valuable resource for researchers and practitioners seeking to understand and address the multifaceted challenge of cognitive fatigue detection.

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“…In the research conducted by Emmanouil Ktistakis et al [22], they explored visual search tasks of varying complexities and durations. The objective of the study was to assess the participants' cognitive workload levels using the subjective NASA-TLX test.…”

Section: Related Work Figure 2 Cognitive Architecturementioning

confidence: 99%

A Comprehensive Analysis of Cognitive CAPTCHAs Through Eye Tracking

Dinh,

Ogiela,

Tran-Trung

et al. 2024

IEEE Access

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CAPTCHA (Completely Automated Public Turing Test to Tell Computers and Humans Apart) has long been employed to combat automated bots. It accomplishes this by utilizing distortion techniques and cognitive characteristics. When it comes to countering security attacks, cognitive CAPTCHA methods have proven to be more effective than other approaches. The advancement of eyetracking technology has greatly improved human-computer interaction (HCI), enabling users to engage with computers without physical contact. This technology is widely used for studying attention, cognitive processes, and performance. In this specific research, we conducted eye-tracking experiments on participants to investigate how their visual behavior changes as the complexity of cognitive CAPTCHAs varies. By analyzing the distribution of eye gaze on each level of CAPTCHA, we can assess users' visual behavior based on eye movement performance and process metrics. The data collected is then employed in Machine Learning (ML) algorithms to categorize and examine the relative importance of these factors in predicting performance. This study highlights the potential to enhance any cognitive CAPTCHA model by gaining insights into the underlying cognitive processes.

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COLET: A dataset for COgnitive workLoad estimation based on eye-tracking

Cited by 16 publications

References 38 publications

Deep Learning-Based Detection of Learning Disorders on a Large Scale Dataset of Eye Movement Records

Deep Learning-Based Detection of Learning Disorders on a Large Scale Dataset of Eye Movement Records

Examining the Landscape of Cognitive Fatigue Detection: A Comprehensive Survey

A Comprehensive Analysis of Cognitive CAPTCHAs Through Eye Tracking

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