Eye tracking

patients to check vision impairment while driving. Eye tracking is the process of measuring either the point of gaze (where one is looking) or the motion of an eye relative to the head. An eye tracker is a device for measuring eye positions and eye movement. Eye trackers are used in research on the visual system, in psychology, in psycholinguistics, marketing, as an input device for human-computer interaction, and in product design. In addition, eye trackers are increasingly being used for assistive and rehabilitative applications such as controlling wheelchairs, robotic arms, and prostheses. Recently, eye tracking has been examined as a tool for the early detection of autism spectrum disorder. There are several methods for measuring eye movement, with the most popular variant using video images to extract eye position. Other methods use search coils or are based on the electrooculogram.

patients to check vision impairment while driving.]] Eye tracking is the process of measuring either the point of gaze (where one is looking) or the motion of an eye relative to the head. An eye tracker is a device for measuring eye positions and eye movement. Eye trackers are used in research on the visual system, in psychology, in psycholinguistics, marketing, as an input device for human-computer interaction, and in product design. In addition, eye trackers are increasingly being used for assistive and rehabilitative applications such as controlling wheelchairs, robotic arms, and prostheses. Recently, eye tracking has been examined as a tool for the early detection of autism spectrum disorder. There are several methods for measuring eye movement, with the most popular variant using video images to extract eye position. Other methods use search coils or are based on the electrooculogram.

Technologies and techniques

The most widely used current designs are video-based eye-trackers. A camera focuses on one or both eyes and records eye movement as the viewer looks at some kind of stimulus. Most modern eye-trackers use the center of the pupil and infrared / near-infrared non-collimated light to create corneal reflections (CR). The vector between the pupil center and the corneal reflections can be used to compute the point of regard on surface or the gaze direction. A simple calibration procedure of the individual is usually needed before using the eye tracker. Two general types of infrared / near-infrared (also known as active light) eye-tracking techniques are used: bright-pupil and dark-pupil. Their difference is based on the location of the illumination source with respect to the optics. If the illumination is coaxial with the optical path, then the eye acts as a retroreflector as the light reflects off the retina creating a bright pupil effect similar to red eye. If the illumination source is offset from the optical path, then the pupil appears dark because the retroreflection from the retina is directed away from the camera. Bright-pupil tracking creates greater iris/pupil contrast, allowing more robust eye-tracking with all iris pigmentation, and greatly reduces interference caused by eyelashes and other obscuring features. It also allows tracking in lighting conditions ranging from total darkness to very bright. Another, less used, method is known as passive light. It uses visible light to illuminate, something which may cause some distractions to users. Another challenge with this method is that the contrast of the pupil is less than in the active light methods, therefore, the center of iris is used for calculating the vector instead. This calculation needs to detect the boundary of the iris and the white sclera (limbus tracking). It presents another challenge for vertical eye movements due to obstruction of eyelids. File:Bright pupil by infrared or near infrared illumination.jpg|Infrared / near-infrared: bright pupil. File:Dark pupil by infrared or near infrared illumination.jpg|Infrared / near-infrared: dark pupil and corneal reflection. File:Visible light eye-tracking algorithm.jpg|Visible light: center of iris (red), corneal reflection (green), and output vector (blue). Eye-tracking setups vary greatly. Some are head-mounted, some require the head to be stable (for example, with a chin rest), and some function remotely and automatically track the head during motion. Most use a sampling rate of at least 30 Hz. Although 50/60 Hz is more common, today many video-based eye trackers run at 240, 350 or even 1000/1250 Hz, speeds needed to capture fixational eye movements or correctly measure saccade dynamics. Eye movements are typically divided into fixations and saccades – when the eye gaze pauses in a certain position, and when it moves to another position, respectively. The resulting series of fixations and saccades is called a scanpath. Smooth pursuit describes the eye following a moving object. Fixational eye movements include microsaccades: small, involuntary saccades that occur during attempted fixation. Most information from the eye is made available during a fixation or smooth pursuit, but not during a saccade. Scanpaths are useful for analyzing cognitive intent, interest, and salience. Because eye movements unfold as a sequence over time, a range of scanpath comparison measures has been developed to quantify the similarity between scanpaths; these measures capture different aspects of scanning behaviour and vary in their suitability for different research questions. Other biological factors (some as simple as gender) may affect the scanpath as well. Eye tracking in human–computer interaction (HCI) typically investigates the scanpath for usability purposes, or as a method of input in gaze-contingent displays, also known as gaze-based interfaces.

Automotive applications

In recent time, eye tracking technology is investigated in automotive domain in both passive and active ways. National Highway Traffic Safety Administration measured glance duration for undertaking secondary tasks while driving and used it to promote safety by discouraging the introduction of excessively distracting devices in vehicles Eye tracking is being explored as a potential method to control IVIS (In-Vehicle Infotainment Systems), the multimedia and navigation systems frequently present in contemporary cars. Though initial research investigated the efficacy of eye tracking system for interaction with HDD (Head Down Display), it still required drivers to take their eyes off the road while performing a secondary task. Recent studies investigated eye gaze controlled interaction with HUD (Head Up Display) that eliminates eyes-off-road distraction. Eye tracking is also used to monitor cognitive load of drivers to detect potential distraction. Though researchers explored different methods to estimate cognitive load of drivers from different physiological parameters, usage of ocular parameters explored a new way to use the existing eye trackers to monitor cognitive load of drivers in addition to interaction with IVIS.

Entertainment applications

The 2021 video game Before Your Eyes registers and reads the player's blinking, and uses it as the main way of interacting with the game.

Engineering applications

The widespread use of eye-tracking technology has shed light to its use in empirical software engineering in the most recent years. The eye-tracking technology and data analysis techniques are used to investigate the understandability of software engineering concepts by the researchers. These include the understandability of business process models, and diagrams used in software engineering such as UML activity diagrams and EER diagrams. Eye-tracking metrics such as fixation, scan-path, scan-path precision, scan-path recall, fixations on area of interest/relevant region are computed, analyzed and interpreted in terms of model and diagram understandability. The findings are used to enhance the understandability of diagrams and models with proper model related solutions and by improving personal related factors such as working-memory capacity, cognitive-load, learning style and strategy of the software engineers and modelers.

Privacy concerns

With eye tracking projected to become a common feature in various consumer electronics, including smartphones, laptops and virtual reality headsets, concerns have been raised about the technology's impact on consumer privacy. With the aid of machine learning techniques, eye tracking data may indirectly reveal information about a user's ethnicity, personality traits, fears, emotions, interests, skills, and physical and mental health condition. If such inferences are drawn without a user's awareness or approval, this can be classified as an inference attack. Eye activities are not always under volitional control, e.g., "stimulus-driven glances, pupil dilation, ocular tremor, and spontaneous blinks mostly occur without conscious effort, similar to digestion and breathing". Therefore, it can be difficult for eye tracking users to estimate or control the amount of information they reveal about themselves.

Notes

References

Romano Bergstrom, Jennifer (2014). Eye Tracking in User Experience Design. Morgan Kaufmann. . Bojko, Aga (2013). Eye Tracking The User Experience (A Practical Guide to Research). Rosenfeld Media. .

Commercial eye tracking

Text derived from https://en.wikipedia.org/w/index.php?oldid=1369021290 under CC BY-SA 4.0; modifications have been made.

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