Search Results (11)

Rapid global urbanization has intensified the demand for sensing solutions that can capture the complex interactions between urban environments and their impact on human physical and mental health. Conventional laboratory-based approaches, while offering high experimental control, often lack ecological validity and fail to represent real-world exposures. To address this gap, we present the eMOTIONAL Cities Walker—a portable multimodal sensing platform designed as a wearable backpack unit developed for the synchronous collecting of multimodal data in either indoor or outdoor settings. The system integrates a suite of environmental sensors (covering microclimate, air pollution and acoustic monitoring) with physiological sensing technologies, including electroencephalography (EEG), mobile eye-tracking and wrist-based physiological monitoring. This configuration enables real-time acquisition of environmental and physiological signals in dynamic, naturalistic settings. Here, we describe the system’s technical architecture, sensor specifications, and field deployment across selected Lisbon locations, demonstrating its feasibility and robustness in urban environments. By bridging controlled laboratory paradigms with ecologically valid real-world sensing, this platform provides a novel tool to advance translational research at the intersection of sensor technology, human experience, and urban health. Full article

This Scoping Review (ScR) synthesizes advances in architectural eye-tracking (ET) research published between 2010 and 2024. Drawing on 75 peer-reviewed studies that met clear inclusion criteria, it monitors the field’s rapid expansion, from only 20 experiments before 2018, to more than 45 new investigations in the three years thereafter, situating these developments within the longer historical evolution of ET hardware and analytical paradigms. The review maps 13 recurrent areas of application, focusing on design evaluation, wayfinding and spatial navigation, end-user experience, and architectural education. Across these domains, ET reliably reveals where occupants focus, for how long, and in what sequence, providing objective evidence that complements designer intuition and conventional post-occupancy surveys. Experts and novices might display distinct gaze signatures; for example, architects spend longer fixating on contextual and structural cues, whereas lay users dwell on decorative details, highlighting possible pedagogical opportunities. Despite these benefits, persistent challenges include data loss in dynamic or outdoor settings, calibration drift, single-user hardware constraints, and the need to triangulate gaze metrics with cognitive or affective measures. Future research directions emphasize integrating ET with virtual or augmented reality (VR) (AR) to validate design interactively, improving mobile tracking accuracy, and establishing shared datasets to enable replication and meta-analysis. Overall, the study demonstrates that ET is maturing into an indispensable, evidence-based lens for creating more intuitive, legible, and human-centered architecture. Full article

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. Full article

In mobile eye-tracking research, the automatic annotation of fixation points is an important yet difficult task, especially in varied and dynamic environments such as outdoor urban landscapes. This complexity is increased by the constant movement and dynamic nature of both the observer and their environment in urban spaces. This paper presents a novel approach that integrates the capabilities of two foundation models, YOLOv8 and Mask2Former, as a pipeline to automatically annotate fixation points without requiring additional training or fine-tuning. Our pipeline leverages YOLO’s extensive training on the MS COCO dataset for object detection and Mask2Former’s training on the Cityscapes dataset for semantic segmentation. This integration not only streamlines the annotation process but also improves accuracy and consistency, ensuring reliable annotations, even in complex scenes with multiple objects side by side or at different depths. Validation through two experiments showcases its efficiency, achieving 89.05% accuracy in a controlled data collection and 81.50% accuracy in a real-world outdoor wayfinding scenario. With an average runtime per frame of 1.61 ± 0.35 s, our approach stands as a robust solution for automatic fixation annotation. Full article

Eye-tracking technologies have matured significantly in recent years and have become more affordable and easier to use. We investigated how eye-tracking technology can be applied to evaluate the usability of mobile augmented reality applications with historical images for urban cultural heritage. The experiment involved a series of complex user evaluation sessions, combining semi-structured interviews, observations, think-aloud protocol, SUS questionnaire, and product reaction cards, complemented by eye tracking, to gather insights on the Spotlight Timisoara AR mobile application, part of a digital storytelling multiplatform for the city of Timisoara (Romania), soon to be European Capital of Culture in 2023. The results indicate strong and weak aspects of the application, both as expressed by the participants and as derived from analyzing the eye-tracking data. The paper also lists the main challenges we identified in using eye-tracking equipment to evaluate the usability of such mobile augmented reality applications for urban outdoor heritage. Full article

Location-based augmented reality technology for real-world, outdoor experiences is rapidly gaining in popularity in a variety of fields such as engineering, education, and gaming. By anchoring medias to geographic coordinates, it is possible to design immersive experiences remotely, without necessitating an in-depth knowledge of the context. However, the creation of such experiences typically requires complex programming tools that are beyond the reach of mainstream users. We introduce BiodivAR, a web cartographic tool for the authoring of location-based AR experiences. Developed using a user-centered design methodology and open-source interoperable web technologies, it is the second iteration of an effort that started in 2016. It is designed to meet needs defined through use cases co-designed with end users and enables the creation of custom geolocated points of interest. This approach enabled substantial progress over the previous iteration. Its reliance on geolocation data to anchor augmented objects relative to the user’s position poses a set of challenges: On mobile devices, GNSS accuracy typically lies between 1 m and 30 m. Due to its impact on the anchoring, this lack of accuracy can have deleterious effects on usability. We conducted a comparative user test using the application in combination with two different geolocation data types (GNSS versus RTK). While the test’s results are undergoing analysis, we hereby present a methodology for the assessment of our system’s usability based on the use of eye-tracking devices, geolocated traces and events, and usability questionnaires. Full article

In this paper, we present a framework for 3D gaze estimation intended to identify the user’s focus of attention in a corneal imaging system. The framework uses a headset that consists of three cameras, a scene camera and two eye cameras: an IR camera and an RGB camera. The IR camera is used to continuously and reliably track the pupil and the RGB camera is used to acquire corneal images of the same eye. Deep learning algorithms are trained to detect the pupil in IR and RGB images and to compute a per user 3D model of the eye in real time. Once the 3D model is built, the 3D gaze direction is computed starting from the eyeball center and passing through the pupil center to the outside world. This model can also be used to transform the pupil position detected in the IR image into its corresponding position in the RGB image and to detect the gaze direction in the corneal image. This technique circumvents the problem of pupil detection in RGB images, which is especially difficult and unreliable when the scene is reflected in the corneal images. In our approach, the auto-calibration process is transparent and unobtrusive. Users do not have to be instructed to look at specific objects to calibrate the eye tracker. They need only to act and gaze normally. The framework was evaluated in a user study in realistic settings and the results are promising. It achieved a very low 3D gaze error (2.12°) and very high accuracy in acquiring corneal images (intersection over union—$IoU$ = 0.71). The framework may be used in a variety of real-world mobile scenarios (indoors, indoors near windows and outdoors) with high accuracy. Full article

Recognizing user emotions while they watch short-form videos anytime and anywhere is essential for facilitating video content customization and personalization. However, most works either classify a single emotion per video stimuli, or are restricted to static, desktop environments. To address this, we propose a correlation-based emotion recognition algorithm (CorrNet) to recognize the valence and arousal (V-A) of each instance (fine-grained segment of signals) using only wearable, physiological signals (e.g., electrodermal activity, heart rate). CorrNet takes advantage of features both inside each instance (intra-modality features) and between different instances for the same video stimuli (correlation-based features). We first test our approach on an indoor-desktop affect dataset (CASE), and thereafter on an outdoor-mobile affect dataset (MERCA) which we collected using a smart wristband and wearable eyetracker. Results show that for subject-independent binary classification (high-low), CorrNet yields promising recognition accuracies: $76.37\%$ and $74.03\%$ for V-A on CASE, and $70.29\%$ and $68.15\%$ for V-A on MERCA. Our findings show: (1) instance segment lengths between 1–4 s result in highest recognition accuracies (2) accuracies between laboratory-grade and wearable sensors are comparable, even under low sampling rates (≤64 Hz) (3) large amounts of neutral V-A labels, an artifact of continuous affect annotation, result in varied recognition performance. Full article

The use of mobile pedestrian wayfinding applications is gaining importance indoors. However, compared to outdoors, much less research has been conducted with respect to the most adequate ways to convey indoor wayfinding information to a user. An explorative study was conducted to compare two pedestrian indoor wayfinding applications, one text-based (SoleWay) and one image-based (Eyedog), in terms of mental effort. To do this, eye tracking data and mental effort ratings were collected from 29 participants during two routes in an indoor environment. The results show that both textual instructions and photographs can enable a navigator to find his/her way while experiencing no or very little cognitive effort or difficulties. However, these instructions must be in line with a user’s expectations of the route, which are based on his/her interpretation of the indoor environment at decision points. In this case, textual instructions offer the advantage that specific information can be explicitly and concisely shared with the user. Furthermore, the study drew attention to potential usability issues of the wayfinding aids (e.g., the incentive to swipe) and, as such, demonstrated the value of eye tracking and mental effort assessments in usability research. Full article

Eye tracking studies have suggested that, when viewing images centrally presented on a computer screen, observers tend to fixate the middle of the image. This so-called ‘central bias’ was later also observed in mobile eye tracking during outdoors navigation, where observers were found to fixate the middle of the head-centered video image. It is unclear, however, whether the extension of the central bias to mobile eye tracking in outdoors navigation may have been due to the relatively long viewing distances towards objects in this task and the constant turning of the body in the direction of motion, both of which may have reduced the need for large amplitude eye movements. To examine whether the central bias in day-to-day viewing is related to the viewing distances involved, we here compare eye movements in three tasks (indoors navigation, tea making, and card sorting), each associated with interactions with objects at different viewing distances. Analysis of gaze positions showed a central bias for all three tasks that was independent of the task performed. These results confirm earlier observations of the central bias in mobile eye tracking data, and suggest that differences in the typical viewing distance during different tasks have little effect on the bias. The results could have interesting technological applications, in which the bias is used to estimate the direction of gaze from head-centered video images, such as those obtained from wearable technology. Full article

Natural outdoor conditions pose unique obstacles for researchers, above and beyond those inherent to all mobile eye-tracking research. During analyses of a large set of eye-tracking data collected on geologists examining outdoor scenes, we have found that the nature of calibration, pupil identification, fixation detection, and gaze analysis all require procedures different from those typically used for indoor studies. Here, we discuss each of these challenges and present solutions, which together define a general method useful for investigations relying on outdoor eye-tracking data. We also discuss recommendations for improving the tools that are available, to further increase the accuracy and utility of outdoor eyetracking data. Full article