An Efficient Method for Extracting Key-Frames from 3D Human Joint Locations for Action Recognition

Kabir, Md. Hasanul; Ahmed, Ferdous; Abdullah-Al-Tariq,

doi:10.1007/978-3-319-20801-5_30

Cited by 1 publication

(1 citation statement)

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“…The latter has two primary objectives. It aims to delete redundant data that will substantially reduce the amount of information to be processed and lower computational costs by diminishing the amount of data that undergoes processing [12,13]. This step helps to decide whether the current frame is a new key frame or not.…”

Section: Composite Engagement Value Feature Extractionmentioning

confidence: 99%

Student behavior analysis to measure engagement levels in online learning environments

Altuwairqi

Jarraya

Allinjawi

et al. 2021

SIViP

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After the COVID-19 pandemic, no one refutes the importance of smart online learning systems in the educational process. Measuring student engagement is a crucial step towards smart online learning systems. A smart online learning system can automatically adapt to learners' emotions and provide feedback about their motivations. In the last few decades, online learning environments have generated tremendous interest among researchers in computer-based education. The challenge that researchers face is how to measure student engagement based on their emotions. There has been an increasing interest towards computer vision and camera-based solutions as technology that overcomes the limits of both human observations and expensive equipment used to measure student engagement. Several solutions have been proposed to measure student engagement, but few are behavior-based approaches. In response to these issues, in this paper, we propose a new automatic multimodal approach to measure student engagement levels in real time. Thus, to offer robust and accurate student engagement measures, we combine and analyze three modalities representing students' behaviors: emotions from facial expressions, keyboard keystrokes, and mouse movements. Such a solution operates in real time while providing the exact level of engagement and using the least expensive equipment possible. We validate the proposed multimodal approach through three main experiments, namely single, dual, and multimodal research modalities in novel engagement datasets. In fact, we build new and realistic student engagement datasets to validate our contributions. We record the highest accuracy value (95.23%) for the multimodal approach and the lowest value of "0.04" for mean square error (MSE).

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Section: Composite Engagement Value Feature Extractionmentioning

confidence: 99%