Research Status: Completed
Researcher: Sunu Wibirama, S.T., M.Eng., IPM.
This research develops an intelligent contactless technology based on artificial intelligence and eye-tracking sensors to support public health education and COVID-19 prevention. The study explores the possibility of replacing conventional eye movement classification methods with modern deep neural network architectures such as BiLSTM and Temporal Convolutional Networks.
The research also focuses on optimizing deep neural network architectures for gaze-based applications through techniques such as pruning, hyperparameter optimization, knowledge distillation, and neural architecture search.
In addition to emphasizing algorithm accuracy and reliability, this study places strong attention on reducing the carbon footprint of the developed technology. The goal is to produce gaze-based applications that are not only intelligent and robust but also environmentally sustainable.
