Adapting Computer Vision Algorithms to Smartphone-based Robot for Education

Esquivel-Barboza, Esteban A.; Llamas, Luis; Gil, Pablo; Bellas, Francisco; Arias-Méndez, Esteban

doi:10.1109/mcna50957.2020.9264289

Cited by 5 publications

(2 citation statements)

References 14 publications

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“…We focused on the applications of CV and mobile systems in various avenues to answer our third research question, such as monitoring student attendance, engaging students in interactive learning experiences, enabling medical training, facilitating remote testing, grading exam papers, tracking student conduct, digitizing printed texts, object detection, face recognition, eye contact monitoring, emotion capturing, gesture recognition, picture categorization, and augmented reality. This study reveals that the use of CV and mobile technologies makes these applications possible [7], [9], [24][25], [30], [37], [39][40], and [49].…”

Section: Applications Of CV and Mobile System In Different Avenuesmentioning

confidence: 85%

“…Jiménez et al [29] conducted a study using NAO robots to teach children with Down syndrome to recognize colors through CV techniques. Esteban et al [30] developed a smartphone-dependent educational robot by integrating CV algorithms into the smartphone's operating system (Android and iOS). The system utilized OpenCV code for ArUco marker detection, basic CV operations for lane detection, and a CNN model based on TensorFlow MobileNetv3 for object identification.…”

Section: Educational Robotsmentioning

confidence: 99%

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Application of Computer Vision and Mobile Systems in Education: A Systematic Review

Rubaiya Tasnim Sholi,

Sarker,

Md. Salman Sohel

et al. 2024

Int. J. Interact. Mob. Technol.

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The computer vision industry has experienced a significant surge in growth, resulting in numerous promising breakthroughs in computer intelligence. The present review paper outlines the advantages and potential future implications of utilizing this technology in education. A total of 84 research publications have been thoroughly scrutinized and analyzed. The study revealed that computer vision technology integrated with a mobile application is exceptionally useful in monitoring students’ perceptions and mitigating academic dishonesty. Additionally, it facilitates the digitization of handwritten scripts for plagiarism detection and automates attendance tracking to optimize valuable classroom time. Furthermore, several potential applications of computer vision technology for educational institutions have been proposed to enhance students’ learning processes in various faculties, such as engineering, medical science, and others. Moreover, the technology can also aid in creating a safer campus environment by automatically detecting abnormal activities such as ragging, bullying, and harassment.

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