An eye tracker based on webcam and its preliminary application evaluation in Chinese reading tests

Lin, Zhongjun; Liu, Yinghua; Wang, Hui; Liu, Zhengji; Cai, Sijin; Zheng, Zhicheng; Zhou, Yongjin; Zhang, Xinyu

doi:10.1016/j.bspc.2022.103521

Cited by 14 publications

(7 citation statements)

References 33 publications

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“…There is no clear prediction in terms of the impact a lower sampling rate can have on classification in the reported studies, but it is important to note that the sampling frequencies of the reported studies are all above 30 Hz: 50 Hz in [ 32 ], 60 Hz in [ 38 ], 100 Hz in [ 33 ], 200 Hz in [ 40 ], and 1000 Hz in [ 39 ]. Considering the advancements in computer vision and the possibilities for eye tracking based on web cameras [ 56 , 57 , 58 ], the results of this paper show promise in accessible dyslexia detection.…”

Section: Discussionmentioning

confidence: 99%

Accessible Dyslexia Detection with Real-Time Reading Feedback through Robust Interpretable Eye-Tracking Features

et al. 2023

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Developing reliable, quantifiable, and accessible metrics for dyslexia diagnosis and tracking represents an important goal, considering the widespread nature of dyslexia and its negative impact on education and quality of life. In this study, we observe eye-tracking data from 15 dyslexic and 15 neurotypical Serbian school-age children who read text segments presented on different color configurations. Two new eye-tracking features were introduced that quantify the amount of spatial complexity of the subject’s gaze through time and inherently provide information regarding the locations in the text in which the subject struggled the most. The features were extracted from the raw eye-tracking data (x, y coordinates), from the original data gathered at 60 Hz, and from the downsampled data at 30 Hz, examining the compatibility of features with low-cost or custom-made eye-trackers. The features were used as inputs to machine learning algorithms, and the best-obtained accuracy was 88.9% for 60 Hz and 87.8% for 30 Hz. The features were also used to analyze the influence of background/overlay color on the quality of reading, and it was shown that the introduced features separate the dyslexic and control groups regardless of the background/overlay color. The colors can, however, influence each subject differently, which implies that an individualistic approach would be necessary to obtain the best therapeutic results. The performed study shows promise in dyslexia detection and evaluation, as the proposed features can be implemented in real time as feedback during reading and show effectiveness at detecting dyslexia with data obtained using a lower sampling rate.

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Section: Discussionmentioning

confidence: 99%

Accessible Dyslexia Detection with Real-Time Reading Feedback through Robust Interpretable Eye-Tracking Features

et al. 2023

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“…From a general perspective, eye tracking technology is used to investigate cognitive functional processes. This has been addressed by Lin et al (2022), who focused on testing the feasibility of a webcam eye tracking interface for commonly used cognitive tasks. They subsequently tested the webcam interface on Chinese reading tasks.…”

Section: Exploring Educational Horizons: Webcam Eye Trackers and Lear...mentioning

confidence: 99%

A Scoping Review of Webcam Eye Tracking in Learning and Education

Dostálová,

Plch

2024

Studia paedagogica

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The use of eye tracking in educational research has shown great potential in recent decades. There are various approaches to the usage of eye tracking technology in this area, including investigation of self-regulated learning from different types of learning environments. Nonetheless, the majority of published research studies have one tremendous limitation: using expensive remote or tower-based eye trackers to provide high-quality data in laboratory conditions. Now, new webcam eye trackers may offer an easily affordable approach allowing eye tracking measurements in the real environment, such as the investigation of learning behavior in online learning environments. The main aim of this scoping review is to explore the use of webcam eye tracking technology in the field of learning and education. We established three specific purposes: 1) to introduce educational topics being explored using webcam eye tracking, 2) to discuss the methodological aspects when exploring educational topics with webcam eye tracking, and 3) to investigate the eye tracking aspects used for the analysis. To do this, we analyzed 16 studies that used webcam eye tracking. The results of the scoping review show that 1) selected studies focus mainly on students’ behavior in online learning environments, such as engagement, lack of attention, cheating and others; 2) a wide range of studies aimed at the development of automatized detection tools; and 3) studies are mainly focused on extracting raw and event data features using them mostly for automatized detection purposes.

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“…Over the years researchers relied on eye movement information for reading detection. While some researchers obtained eye-movement information using camera-based eye trackers [17,27,28], some relied on the EOG signal [29][30][31]. Researchers explored reading detection in different controlled environments in the office, in transit and also during social interactions [32][33][34][35].…”

Section: A Eog-based Reading Detectionmentioning

confidence: 99%

EOG-Based Reading Detection in the Wild Using Spectrograms and Nested Classification Approach

Baray,

Ahmed,

Chowdhury

et al. 2023

IEEE Access

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Electrooculography, also known as EOG, is a technique that is used to calculate the corneo-retinal standing potential, which is located between the cornea and the retina of the human eye. Applications of EOG include eye disease diagnosis and eye movement tracking. There has been various research on reading activity detection from EOG signals in controlled or laboratory settings. However, determining reading behaviours from data collected from real-world environments remains a challenging problem. Reading detection in practical scenarios can lead us to track our daily reading activity, in turn improving our learning experience and even workplace productivity. Tracking regular reading behaviour can also lead to further research in cognitive psychology, literacy development, reading motivation, and reading comprehension. In this study, we investigated an electrooculogram dataset that was collected on the field from 10 users who were engaged in their daily activities on two separate days. We propose a pipeline combining the statistical features with deep learning features from pretrained ImageNet models. To detect the fine-grained reading activities, we employed a nested classification approach where we detect reading and not reading at first and then do one more step of classification to discriminate among three different reading activities. With our pipeline, we could achieve 66.56% accuracy in detecting the reading activities whereas the original dataset publication showed a baseline performance of only 32%.

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An eye tracker based on webcam and its preliminary application evaluation in Chinese reading tests

Cited by 14 publications

References 33 publications

Accessible Dyslexia Detection with Real-Time Reading Feedback through Robust Interpretable Eye-Tracking Features

Accessible Dyslexia Detection with Real-Time Reading Feedback through Robust Interpretable Eye-Tracking Features

A Scoping Review of Webcam Eye Tracking in Learning and Education

EOG-Based Reading Detection in the Wild Using Spectrograms and Nested Classification Approach

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