“…Since the previous two mentioned approaches rely on some sensors such as infrared or electrodes, they can be grouped under the term sensor-based eye tracking technologies. The other eye-tracking methods rely on computer-vision techniques for detecting and tracking the human eyes in the captured video frames by a camera in real-time without direct contact with human eyes or using extra hardware sensors 31 – 33 . Figure 1 Eye-tracking systems for patients with speech impairments.…”
Eye-based communication languages such as Blink-To-Speak play a key role in expressing the needs and emotions of patients with motor neuron disorders. Most invented eye-based tracking systems are complex and not affordable in low-income countries. Blink-To-Live is an eye-tracking system based on a modified Blink-To-Speak language and computer vision for patients with speech impairments. A mobile phone camera tracks the patient’s eyes by sending real-time video frames to computer vision modules for facial landmarks detection, eye identification and tracking. There are four defined key alphabets in the Blink-To-Live eye-based communication language: Left, Right, Up, and Blink. These eye gestures encode more than 60 daily life commands expressed by a sequence of three eye movement states. Once the eye gestures encoded sentences are generated, the translation module will display the phrases in the patient’s native speech on the phone screen, and the synthesized voice can be heard. A prototype of the Blink-To-Live system is evaluated using normal cases with different demographic characteristics. Unlike the other sensor-based eye-tracking systems, Blink-To-Live is simple, flexible, and cost-efficient, with no dependency on specific software or hardware requirements. The software and its source are available from the GitHub repository (https://github.com/ZW01f/Blink-To-Live).
“…Since the previous two mentioned approaches rely on some sensors such as infrared or electrodes, they can be grouped under the term sensor-based eye tracking technologies. The other eye-tracking methods rely on computer-vision techniques for detecting and tracking the human eyes in the captured video frames by a camera in real-time without direct contact with human eyes or using extra hardware sensors 31 – 33 . Figure 1 Eye-tracking systems for patients with speech impairments.…”
Eye-based communication languages such as Blink-To-Speak play a key role in expressing the needs and emotions of patients with motor neuron disorders. Most invented eye-based tracking systems are complex and not affordable in low-income countries. Blink-To-Live is an eye-tracking system based on a modified Blink-To-Speak language and computer vision for patients with speech impairments. A mobile phone camera tracks the patient’s eyes by sending real-time video frames to computer vision modules for facial landmarks detection, eye identification and tracking. There are four defined key alphabets in the Blink-To-Live eye-based communication language: Left, Right, Up, and Blink. These eye gestures encode more than 60 daily life commands expressed by a sequence of three eye movement states. Once the eye gestures encoded sentences are generated, the translation module will display the phrases in the patient’s native speech on the phone screen, and the synthesized voice can be heard. A prototype of the Blink-To-Live system is evaluated using normal cases with different demographic characteristics. Unlike the other sensor-based eye-tracking systems, Blink-To-Live is simple, flexible, and cost-efficient, with no dependency on specific software or hardware requirements. The software and its source are available from the GitHub repository (https://github.com/ZW01f/Blink-To-Live).
“…In order to track gaze of the user, in addition to location of the PC, GC location is required to establish a mapping between PC-GC vector and screen [11]. In this work we detect GC location with the same algorithm as of PC detection.…”
In this paper, a multi-function embedded eye tracker system (ETS) has been presented and FPGA implementation of that has been described. The image processing algorithm is based on morphological operations to detect centers of pupil (PC) and corneal glint (GC) so that gaze of the user can be estimated by establishing a mapping between obtained PC-GC vector and screen. Moreover, the proposed ETS is capable to detect pupil size which is vitally important in understanding consciousness of the user since it varies in response to light condition. Taking advantage of optimum system design and parallel signal processing on high-speed digital units of the FPGA, the processing time of 1.8 ms was achieved that allows operation of the system up to 500 fps. The results of test with 176 × 120 images demonstrate maximum vertical or horizontal error of ±1 pixel. Experimental results imply versatility of the proposed low-power and highaccuracy ETS for real-time and accurate applications such as medical diagnosis and human-computer interface.
INDEX TERMSEmbedded system, Field programmable gate arrays, Gaze tracking, Hardware acceleration, Morphological operations.
“…This includes behavioral data such as intonation of speech (Linneman, 2013), the contents of conversations (Turmunkh et al., 2019), and, as we investigate here, response times. In addition, it is also possible to extract physiological data from video including facial expression (Canedo & Neves, 2019; Kumari et al., 2015), gaze location (Liu et al., 2022), and breathing (Janssen et al., 2015; Tran et al., 2017). Even heart rate can, in some cases, be extracted from video by measuring changes in the redness of the skin (Wu et al., 2012).…”
Television game shows have proven to be a valuable resource for studying human behavior under conditions of high stress and high stakes. However, previous work has focused mostly on choices—ignoring much of the rich visual information that is available on screen. Here, we take a first step to extracting more of this information by investigating the response times and blinking of contestants in the BBC show Mastermind. In Mastermind, contestants answer rapid‐fire quiz questions while a camera slowly zooms in on their faces. By labeling contestants' behavior and blinks from 25 episodes, we asked how accuracy, response times, and blinking varied over the course of the game. For accuracy and response times, we tested whether contestants responded more accurately and more slowly after an error—exhibiting the “post‐error increase in accuracy” and “post‐error slowing” which has been repeatedly observed in the lab. For blinking, we tested whether blink rates varied according to the cognitive demands of the game—decreasing during periods of cognitive load, such as when pondering a response, and increasing at event boundaries in the task, such as the start of a question. In contrast to the lab, evidence for post‐error changes in accuracy and response time was weak, with only marginal effects observed. In line with the lab, blinking varied over the course of the game much as we predicted. Overall, our findings demonstrate the potential of extracting dynamic signals from game shows to study the psychophysiology of behavior in the real world.
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