Mobile eye tracking is a technology that measures where a person looks while moving freely, providing insight into visual attention in real-world situations. Unlike stationary systems that require a person to remain still, mobile eye tracking allows for the study of natural behavior as it happens. This capability has expanded its use for understanding how people interact with their environment.
The Technology Behind Mobile Eye Tracking
Mobile eye tracking technology uses wearable devices, like glasses or headsets, equipped with miniature cameras and infrared (IR) light sources. The IR light illuminates the eye, creating reflections on the cornea. Cameras record these reflections and the pupil’s center, a method known as Pupil Center Corneal Reflection (PCCR), to track eye movement.
An onboard computer or connected device uses algorithms to process this data, calculating the gaze direction from the relationship between the pupil’s center and the corneal reflection. Many trackers also include a forward-facing scene camera to record the user’s field of view. This allows the system to map gaze points directly onto the real-world scene.
The process generates a continuous stream of gaze data. While dedicated glasses are common, the technology is also integrated into virtual reality (VR) and augmented reality (AR) headsets. In these devices, eye tracking can enhance immersion or allow interaction with virtual objects. The use of IR light allows the technology to function in various lighting conditions.
Applications Across Various Fields
In market research and advertising, mobile eye tracking reveals how consumers engage with products on store shelves or view ads in public spaces. Researchers can see which packaging elements draw the eye or how long a person looks at a display. This provides direct insight into what captures consumer interest in a retail environment.
For user experience (UX) design, mobile eye tracking helps evaluate the usability of mobile apps and websites. Developers can observe how users navigate an interface, where they hesitate, and which features they overlook. This information helps in creating more intuitive and efficient digital products.
Healthcare and medical research use this technology to study neurological conditions by analyzing a patient’s gaze patterns for diagnostic clues. In surgical training, eye trackers assess the visual strategies of trainee surgeons. These are then compared to those of experts to refine educational techniques.
The technology also applies to sports performance, where analyzing an athlete’s gaze can improve training. For instance, coaches can study where a quarterback looks before a pass. In education, it helps researchers understand student engagement with learning materials, while the automotive industry uses it to study driver attention and improve in-car systems.
Understanding Mobile Eye Tracking Data
Raw data from a mobile eye tracker consists of coordinates corresponding to gaze points. This data is processed into visualizations, with one of the most common being the gaze plot, or scan path. Gaze plots show the sequence of eye movements, connecting dots (fixations) with lines representing the quick jumps between them (saccades).
Another visualization is the heatmap, which aggregates gaze data to show which parts of a scene received the most visual attention. Warmer colors like red indicate a high concentration of fixations, while cooler colors like green or yellow show less attention. Areas with no color were likely not viewed.
To quantify attention, researchers define Areas of Interest (AOIs), which are specific regions within the recorded scene, like a product on a shelf. By drawing a boundary around an AOI, analysts can extract metrics. These include how long it took a user to first look at the area or the total time spent looking within it.
Other metrics can also be used. The duration and count of fixations can suggest the level of interest or cognitive processing. Changes in pupil dilation can also be correlated with cognitive load or emotional arousal.
Practical Usage and Ethical Aspects
To ensure accuracy, a calibration process is necessary for each user before data collection. This involves the user looking at a series of specific points. This process allows the system to map the eye’s movements to corresponding gaze points in the environment.
Environmental conditions can influence data quality. While IR illumination helps, extreme lighting changes or direct sunlight can interfere with the cameras. Because the devices are wearable, rapid head movements can also cause the glasses to shift, which may require recalibration.
The use of mobile eye tracking also brings ethical responsibilities concerning privacy. The technology captures everything a person sees, which can include sensitive information. Researchers must obtain informed consent from participants, explaining what data is collected and how it will be used. Data anonymization and secure storage are also necessary to protect participant privacy.