Eye Strain Detection During Online Learning

计算机科学 过程(计算) 2019年冠状病毒病(COVID-19) 人工智能 医学 疾病 病理 操作系统 传染病(医学专业)
作者
Le Thi Phuong Thao,Duong Ba Cuong,Vu Ngoc Hung,Le Anh Vinh,Doan Trung Nghia,Dinh Tuan Hai,Nguyen Bich Nhi
标识
DOI:10.32604/iasc.2023.031026
摘要

The recent outbreak of the coronavirus disease of 2019 (Covid-19) has been causing many disruptions among the education systems worldwide, most of them due to the abrupt transition to online learning. The sudden upsurge in digital electronic devices usage, namely personal computers, laptops, tablets and smartphones is unprecedented, which leads to a new wave of both mental and physical health problems among students, for example eye-related illnesses. The overexposure to electronic devices, extended screen time usage and lack of outdoor sunlight have put a consequential strain on the student’s ophthalmic health because of their young age and a relative lack of responsibility on their own health. Failure to take appropriate external measures to mitigate the negative effects of this process could lead to common ophthalmic illnesses such as myopia or more serious conditions. To remedy this situation, we propose a software solution that is able to track and capture images of its users’ eyes to detect symptoms of eye illnesses while simultaneously giving them warnings and even offering treatments. To meet the requirements of a small and light model that is operable on low-end devices without information loss, we optimized the original MobileNetV2 model with depth-wise separable convolutions by altering the parameters in the last layers with an aim to minimize the resizing of the input image and obtained a new model which we call EyeNet. Combined with applying the knowledge distillation technique and ResNet-18 as a teacher model to train the student model, we have successfully increased the accuracy of the EyeNet model up to 87.16% and support the development of a model compatible with embedded systems with limited computing power, accessible to all students.

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