Object-gaze distance: Quantifying near-peripheral gaze behavior in real-world applications

Abstract

Eye tracking (ET) has shown to reveal the wearer’s cognitive processes using the measurement of the central point of foveal vision. However, traditional ET evaluation methods have not been able to take into account the wearers’ use of the peripheral field of vision. We propose an algorithmic enhancement to a state-of-the-art ET analysis method, the Object-Gaze Distance (OGD), which additionally allows the quantification of near-peripheral gaze behavior in complex real-world environments. The algorithm uses machine learning for area of interest (AOI) detection and computes the minimal 2D Euclidean pixel distance to the gaze point, creating a continuous gaze-based time-series. Based on an evaluation of two AOIs in a real surgical procedure, the results show that a considerable increase of interpretable fixation data from 23.8 % to 78.3 % of AOI screw and from 4.5 % to 67.2 % of AOI screwdriver was achieved, when incorporating the near-peripheral field of vision. Additionally, the evaluation of a multi-OGD time series representation has shown the potential to reveal novel gaze patterns, which may provide a more accurate depiction of human gaze behavior in multi-object environments.

What we see is what we do: a practical Peripheral Vision-Based HMM framework for gaze-enhanced recognition of actions in a medical procedural task

“…Higher noise distortions affect the ability of the HMM to accurately detect the correct action and lead to overall lower performance since it becomes more likely that an OOI Hit can be falsely detected as either a "no Hit," or as an OOI Hit on a different OOI within proximity. Conversely, as the OGD feature does not rely on binary gaze-on-target logic, it shows remarkably higher robustness to noise, confirming suggestions by Wang et al (2021) that the quantification of peripheral gaze information can be used effectively for EAR.…”

“…extracted and transformed for HMM input. In this paper, we investigated the action recognition performance using two different gaze features, which were extracted as follows: The trained model and the gaze coordinates of each recording are concatenated using the cGOM (see Wolf et al 2018) and the OGD (see Wang et al 2021) algorithm. The cGOM algorithm matches the gaze point with the detected OOIs to create a list of OOI Hits.…”

“…In their recent work, Wang et al (2021) have presented a way to increase the informational value of gaze data by using automated, machine learning-assisted computation of a 2D Euclidean distance between each OOI and the gaze point for each fixation. The calculation of distances to the gaze point allows for the simultaneous acquisition of time-series data for all OOIs, thus multiplying the object-related gaze data by the number of OOIs.…”

“…Even though they have shown that ML models could vastly benefit from exploiting human attention during human-object interaction (Rong et al 2021), eye movements have been largely neglected as a suitable feature input for EAR models. Recent publications on automated neural network-based object-gaze mapping methods (Wolf et al 2018) and subsequent developments of advanced gaze metrics such as the object-gaze distance (OGD), which captures information of the user's peripheral field of vision by representing a relative distance of the gaze point to task-related objects (Wang et al 2021), serve as an inspiration to explore the relevance of eye movements as data modality for EAR.…”

Gaze is more than just a point: Rethinking visual attention analysis using peripheral vision-based gaze mapping

“…Furthermore, annotators are commonly limited to gaze point-based object mapping, which reduces the human gaze from concentric fields with varying visual acuity, to the center point of the foveal [35,42]. Consequently, conventional fixation and saccade-based measures may only be mapped to a single object at a time [36,39]. In recent years, advances in image processing and machine learning (ML) have produced automated mapping algorithms, such as cGOM [41] and Deep-SAGA [12].…”