Eye Gaze-based Early Intent Prediction Utilizing CNN-LSTM

In assistive technologies designed for patients with extremely limited motor or communication capabilities, it is of significant importance to accurately predict the intention of the user, in a timely manner. This paper presents a new framework for the early prediction of the user's intent via their eye gaze. The seen objects in the displayed images, and the order of their selection are identified from the spatial and temporal information of the gaze. By employing a combination of convolution neuronal network (CNN) and long short term memory (LSTM), early prediction of the user's intention is enabled. The proposed framework is tested using experimental data obtained from eight subjects. Results demonstrate an average accuracy of 82.27% across all considered intended tasks for early prediction, confirming the effectiveness of the proposed method.