Introducing Point-of-Interest as an alternative to Area-of-Interest for fixation duration analysis

Quantifying Dwell Time With Location-based Augmented Reality: Dynamic AOI Analysis on Mobile Eye Tracking Data With Vision Transformer

Mobile eye tracking captures egocentric vision and is well-suited for naturalistic studies. However, its data is noisy, especially when acquired outdoor with multiple participants over several sessions. Area of interest analysis on moving targets is difficult because A) camera and objects move nonlinearly and may disappear/reappear from the scene; and B) off-the-shelf analysis tools are limited to linearly moving objects. As a result, researchers resort to time-consuming manual annotation, which limits the use of mobile eye tracking in naturalistic studies. We introduce a method based on a fine-tuned Vision Transformer (ViT) model for classifying frames with overlaying gaze markers. After fine-tuning a model on a manually labelled training set made of 1.98% (=7845 frames) of our entire data for three epochs, our model reached 99.34% accuracy as evaluated on hold-out data. We used the method to quantify participants' dwell time on a tablet during the outdoor user test of a mobile augmented reality application for biodiversity education. We discuss the benefits and limitations of our approach and its potential to be applied to other contexts.

Identification of Urban Functional Areas and Their Mixing Degree Using Point of Interest Analyses

With the rise of smart cities and geographic big-data applications, the refined identification of urban functional areas is of great significance for decision-makers to formulate scientific and reasonable urban planning. In this paper, a random forest algorithm was adopted to analyze Point of Interest (POI) data, with the aim of identifying the functional zoning of Chongqing’s central urban area and to quantify the functional mixing degree by combining POI data with Open Street Map (OSM) road networks. The main conclusions include: (1) Due to the topography and previous urban planning strategies, the central urban area of Chongqing has a significant cluster development that radiates outward from the center of each district. Mixed functional areas account for about 40% of the total area, excluding non-functional areas. The land-use intensity of the central urban area is significant. (2) The mixing degree of the inner ring is generally high, while the aggregation characteristics of the outer ring are weaker. The functions of catering and transportation are dispersed and are mutually exclusive from other functions. (3) The identification of residential service and green spaces and squares was the best, while the identification of catering service areas was slightly less accurate. The overall identification accuracy of the single-function areas was 82%. The results of functional zoning provide valuable information for understanding the downtown area of Chongqing and represent a new method for the study of urban structures in the future.

A Framework to Evaluate Areas of Interest for Sustainable Products and Designs

Experience and evaluation research on sustainable products’ design is increasingly supported by eye-tracking tools. In particular, many studies have investigated the effect of gazing at or fixating on Areas of Interest on products’ evaluations, and in a number of cases, they have inferred the critical graphical elements leading to the preference of sustainable products. This paper is motivated by the lack of generalizability of the results of these studies, which have predominantly targeted specific products and Areas of Interest. In addition, it has also been overlooked that the observation of some Areas of Interest, despite not specifically targeting sustainable aspects, can lead consumers to prefer or appreciate sustainable products in any case. Furthermore, it has to be noted that sustainable products can be recognized based on their design (shape, material, lack of waste generated) and/or, more diffusedly, information clearly delivered on packaging and in advertising. With reference to the latter, this paper collected and classified Areas of Interest dealt with in past studies, markedly in eco-design and green consumption, and characterized by their potential generalizability. Specifically, the identified classes of Areas of Interest are not peculiar to specific products or economic sectors. These classes were further distinguished into “Content”, i.e., the quality aspect they intend to highlight, and “Form”, i.e., the graphical element used as a form of communication. This framework of Areas of Interest is the major contribution of the paper. Such a framework is needed to study regularities across multiple product categories in terms of how the observation of Areas of Interest leads to product appreciation and value perception. In addition, the potential significant differences between sustainable and commonplace products can be better investigated.

How to choose the size of facial areas of interest in interactive eye tracking

Advances in eye tracking technology have enabled the development of interactive experimental setups to study social attention. Since these setups differ substantially from the eye tracker manufacturer's test conditions, validation is essential with regard to the quality of gaze data and other factors potentially threatening the validity of this signal. In this study, we evaluated the impact of accuracy and areas of interest (AOIs) size on the classification of simulated gaze (fixation) data. We defined AOIs of different sizes using the Limited-Radius Voronoi-Tessellation (LRVT) method, and simulated gaze data for facial target points with varying accuracy. As hypothesized, we found that accuracy and AOI size had strong effects on gaze classification. In addition, these effects were not independent and differed in falsely classified gaze inside AOIs (Type I errors; false alarms) and falsely classified gaze outside the predefined AOIs (Type II errors; misses). Our results indicate that smaller AOIs generally minimize false classifications as long as accuracy is good enough. For studies with lower accuracy, Type II errors can still be compensated to some extent by using larger AOIs, but at the cost of more probable Type I errors. Proper estimation of accuracy is therefore essential for making informed decisions regarding the size of AOIs in eye tracking research.

Data-driven analysis of gaze patterns in face perception: Methodological and clinical contributions

Gaze patterns during face perception have been shown to relate to psychiatric symptoms. Standard analysis of gaze behavior includes calculating fixations within arbitrarily predetermined areas of interest. In contrast to this approach, we present an objective, data-driven method for the analysis of gaze patterns and their relation to diagnostic test scores. This method was applied to data acquired in an adult sample (N = 111) of psychiatry outpatients while they freely looked at images of human faces. Dimensional symptom scores of autism, attention deficit, and depression were collected. A linear regression model based on Principal Component Analysis coefficients computed for each participant was used to model symptom scores. We found that specific components of gaze patterns predicted autistic traits as well as depression symptoms. Gaze patterns shifted away from the eyes with increasing autism traits, a well-known effect. Additionally, the model revealed a lateralization component, with a reduction of the left visual field bias increasing with both autistic traits and depression symptoms independently. Taken together, our model provides a data-driven alternative for gaze data analysis, which can be applied to dimensionally-, rather than categorically-defined clinical subgroups within a variety of contexts. Methodological and clinical contribution of this approach are discussed.