EyeHelp: Predicting an AOI based on Eye Gaze for Patient Assistance

作     者：Akshay S Dhanush S Aswin G Nath Amudha J 

作者机构：Department of Computer Science School of Computing Amrita Vishwa Vidyapeetham Mysuru Campus Karnataka India Computational Engineering Ruhr University Bochum Bochum Germany Department of Computer Science and Engineering School of Computing Amrita Vishwa Vidyapeetham Bengaluru Karnataka India 

出 版 物：《Procedia Computer Science》 

年 卷 期：2025年第258卷

页      面：1486-1495页

主　　题：Eye Tracking Assistance Deep Learning Prediction Area Of Interest(AOI) Eye Gaze Gaze Pattern 

摘      要：In this research, we delve into the predictive capabilities of deep learning models for classifying Areas of Interest (AOIs) based on gaze patterns in patient assistance systems. Utilizing eye-tracking technology to capture gaze data consisting of x and y coordinates along with duration, we aim to discern the specific regions of interest on the screen. With 9 distinct AOIs identified through a 9-point calibration process, we leveraged Python to preprocess the dataset. Applying FFNN, LSTM, and GRU, EyeHelp achieved promising accuracies: 80% for FFNN, 96% for LSTM, and 93% for GRU. Comprehensive observations in terms of accuracy, recall, and F1-score metrics across AOI classes are provided. EyeHelp employs an open source integrated webcam; hence the approach is open and adaptable to various healthcare environments. By integrating this technology, our system can be seamlessly deployed in clinics, hospitals, and even home care environments. The utilization of integrated webcams facilitates cost-effective and non-intrusive monitoring, offering valuable insights into patient behavior and attention patterns.