Mobile eye tracking is an important tool in psychology and human-centred interaction design for understanding how people process visual scenes and user interfaces. However, analysing recordings from mobile eye trackers, which typically include an egocentric video of the scene and a gaze signal, is a time-consuming and largely manual process. To address this challenge, we propose a web-based annotation tool that leverages few-shot image classification and interactive machine learning (IML) to accelerate the annotation process. The tool allows users to efficiently map fixations to areas of interest (AOI) in a video-editing-style interface. It includes an IML component that generates suggestions and learns from user feedback using a few-shot image classification model initialised with a small number of images per AOI. Our goal is to improve the efficiency and accuracy of fixation-to-AOI mapping in mobile eye tracking.

A scanpath is an important concept in eye tracking that represents a person’s eye movements in a graph-like structure. Passive gaze-based interfaces, in which users do not consciously interact using their eyes, typically interpret users’ scanpaths to enable adaptive and personalised interaction. Despite the benefits of graph neural networks (GNNs) in graph processing, this technology has not been considered for that purpose. An example application is perceived relevance estimation, which still suffers from low classification performance. In this work, we investigate how and whether GNNs can be used to analyse scanpaths for readers’ perceived relevance estimation using the gazeRE dataset. This dataset contains eye tracking data from 24 participants, who rated the relevance of 12 short and 12 long documents in relation to a given query. The relevance was assigned either to an entire short document or to each paragraph within a long document, which allowed us to investigate two different GNN tasks. For comparison, we reproduced the gazeRE baseline using Random Forest and Support Vector classifiers, and an additional Convolutional Neural Network (CNN) from the literature. All models were evaluated using leave-users-out cross-validation. For short documents, the GNNs surpassed the baseline methods, with certain experiments showing an absolute balanced accuracy improvement of 7.6% and 14.3% over the CNN and gazeRE baselines, respectively. However, similar improvements were not observed in long documents. This work investigates and discusses the future potential of using GNNs as a scanpath analysis method for passive gaze-based applications, such as implicit relevance estimation.

Mobile eye tracking studies involve analyzing areas of interest (AOIs) and visual attention to these AOIs to understand how people process visual information. However, accurately annotating the data collected for user studies can be a challenging and time-consuming task. Current approaches for automatically or semi-automatically analyzing head-mounted eye tracking data in mobile eye tracking studies have limitations, including a lack of annotation flexibility or the inability to adapt to specific target domains. To address this problem, we present IMETA, an architecture for semi-automatic fixation-to-AOI mapping. When an annotator assigns an AOI label to a sequence of frames based on the respective fixation points, an interactive video object segmentation method is used to estimate the mask proposal of the AOI. Then, we use the 3D reconstruction of the visual scene created from the eye tracking video to map these AOI masks to 3D. The resulting 3D segmentation of the AOI can be used to suggest labels for the rest of the video, with the suggestions becoming increasingly accurate as more samples are provided by an annotator using interactive machine learning (IML). IMETA has the potential to reduce the annotation workload and speed up the evaluation of mobile eye tracking studies.

Mobile eye tracking is an important tool in psychology and human-centered interaction design for understanding how people process visual scenes and user interfaces. However, analyzing recordings from head-mounted eye trackers, which typically include an egocentric video of the scene and a gaze signal, is a time-consuming and largely manual process. To address this challenge, we develop eyeNotate, a web-based annotation tool that enables semi-automatic data annotation and learns to improve from corrective user feedback. Users can manually map fixation events to areas of interest (AOIs) in a video-editing-style interface (baseline version). Further, our tool can generate fixation-to-AOI mapping suggestions based on a few-shot image classification model (IML-support version). We conduct an expert study with trained annotators (n = 3) to compare the baseline and IML-support versions. We measure the perceived usability, annotations’ validity and reliability, and efficiency during a data annotation task. We asked our participants to re-annotate data from a single individual using an existing dataset (n = 48). Further, we conducted a semi-structured interview to understand how participants used the provided IML features and assessed our design decisions. In a post hoc experiment, we investigate the performance of three image classification models in annotating data of the remaining 47 individuals.

The pervasive integration of artificial intelligence (AI) into daily life has led to a growing interest in AI agents that can learn continuously. Interactive Machine Learning (IML) has emerged as a promising approach to meet this need, essentially involving human experts in the model training process, often through iterative user feedback. However, repeated feedback requests can lead to frustration and reduced trust in the system. Hence, there is increasing interest in refining how these systems interact with users to ensure efficiency without compromising user experience. Our research investigates the potential of eye tracking data as an implicit feedback mechanism to detect user disagreement with AI-generated captions in image captioning systems. We conducted a study with 30 participants using a simulated captioning interface and gathered their eye movement data as they assessed caption accuracy. The goal of the study was to determine whether eye tracking data can predict user agreement or disagreement effectively, thereby strengthening IML frameworks. Our findings reveal that, while eye tracking shows promise as a valuable feedback source, ensuring consistent and reliable model performance across diverse users remains a challenge.