A camera-based attention level assessment tool designed for classroom usage

Tseng, Chun-Hsiung; Chen, Yung-Hui

doi:10.1007/s11227-017-2122-7

Cited by 12 publications

(5 citation statements)

References 9 publications

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“…Different approaches have been proposed for generating attention estimation datasets and corresponding annotations. The most common way for researchers is to record their own datasets and hand label the images with information regarding the direction of the gaze (Tseng and Cheng, 2017) or shifts of attention between targets over the images in the dataset (Steil et al, 2018). All of the previously mentioned datasets and their annotations can be used to estimate the fo-cus of attention, through the eye gaze and head pose labels.…”

Section: Datasets and Annotation Methodsmentioning

confidence: 99%

Subjective Annotations for Vision-based Attention Level Estimation

Coifman¹,

Rohoska

Kristoffersen

et al. 2019

Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Application

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Attention level estimation systems have a high potential in many use cases, such as human-robot interaction, driver modeling and smart home systems, since being able to measure a person's attention level opens the possibility to natural interaction between humans and computers. The topic of estimating a human's visual focus of attention has been actively addressed recently in the field of HCI. However, most of these previous works do not consider attention as a subjective, cognitive attentive state. New research within the field also faces the problem of the lack of annotated datasets regarding attention level in a certain context. The novelty of our work is two-fold: First, we introduce a new annotation framework that tackles the subjective nature of attention level and use it to annotate more than 100,000 images with three attention levels and second, we introduce a novel method to estimate attention levels, relying purely on extracted geometric features from RGB and depth images, and evaluate it with a deep learning fusion framework. The system achieves an overall accuracy of 80.02%. Our framework and attention level annotations are made publicly available.

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Section: Datasets and Annotation Methodsmentioning

confidence: 99%

Subjective Annotations for Vision-based Attention Level Estimation

Coifman¹,

Rohoska

Kristoffersen

et al. 2019

Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Application

View full text Add to dashboard Cite

show abstract

“…Inter-judge reliability was computed using Cohen’s kappa for all possible pairs of judges. Additionally, the study in [ 43 ] attempts to detect three levels of Cognitive Load (CL) in participants taking an arithmetic test under the presence and absence of affective interference (e.g., arousal and other emotional and psychological factors). For example, it is human nature to have a mixture of high cognitive load and personal feelings (e.g., frustration, confusion) when introduced to complicated tasks.…”

Section: Related Workmentioning

confidence: 99%

The Automatic Detection of Cognition Using EEG and Facial Expressions

Kerdawy

Halaby

Hassan

et al. 2020

Sensors

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Detecting cognitive profiles is critical to efficient adaptive learning systems that automatically adjust the content delivered depending on the learner’s cognitive states and skills. This study explores electroencephalography (EEG) and facial expressions as physiological monitoring tools to build models that detect two cognitive states, namely, engagement and instantaneous attention, and three cognitive skills, namely, focused attention, planning, and shifting. First, while wearing a 14-channel EEG Headset and being videotaped, data has been collected from 127 subjects taking two scientifically validated cognitive assessments. Second, labeling was performed based on the scores obtained from the used tools. Third, different shallow and deep models were experimented in the two modalities of EEG and facial expressions. Finally, the best performing models for the analyzed states are determined. According to the used performance measure, which is the f-beta score with beta = 2, the best obtained results for engagement, instantaneous attention, and focused attention are EEG-based models with 0.86, 0.82, and 0.63 scores, respectively. As for planning and shifting, the best performing models are facial expressions-based models with 0.78 and 0.81, respectively. The obtained results show that EEG and facial expressions contain important and different cues and features about the analyzed cognitive states, and hence, can be used to automatically and non-intrusively detect them.

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“…Therefore, multiple studies that uses camera-based tools have been conducted to help determine student's attention level during online classes. One of the method uses an electroencephalogram (EEG) meter to determine a student's attention level [3]. With the EEG meter equipped on the user, the camera will take a picture of the user and let the picture be examined by the algorithm in the system to determine the attention level.…”

Section: Introductionmentioning

confidence: 99%

The application of image processing for monitoring student’s attention level during online class

Ong

Chew

Phang

2021

J. Phys.: Conf. Ser.

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With the COVID-19 pandemic still causing the world to be quarantined in their house to prevent the spread of the virus, this means online classes are still the main method of conducting classes. This project aims to help lecturers monitor the students during class as they are having problems checking whether the students are paying attention or not. This project uses the student’s facial features to determine their attention level using two different coding algorithm Viola-Jones and Sobel edge. These two algorithms help to determine what kind of facial expression that the students are making. The Viola-Jones algorithm detects and captures the student’s facial features such as eyes and mouth while the Sobel edge algorithm detects the edges of the facial features to determine whether the eyes and mouth are open or closed. With the data collected it will run through the database to determine the student’s attention level and inform the lecturer.

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A camera-based attention level assessment tool designed for classroom usage

Cited by 12 publications

References 9 publications

Subjective Annotations for Vision-based Attention Level Estimation

Subjective Annotations for Vision-based Attention Level Estimation

The Automatic Detection of Cognition Using EEG and Facial Expressions

The application of image processing for monitoring student’s attention level during online class

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