Neural Network Based Eye Tracking

Morozkin, Pavel; Swynghedauw, Marc; Trocan, Maria

doi:10.1007/978-3-319-67077-5_58

Cited by 8 publications

(2 citation statements)

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“…There are different eye-tracking methods and tools and they do not use the same tracking approach, hence not allowing tracking of the same behaviors. Consequently, further investigation is needed to achieve an appropriate network construction, followed by more efficient training to avoid common failures, such as over-training (e.g., Morozkin et al, 2017).…”

Section: Challenges Of Aietmentioning

confidence: 99%

Eye tracking and artificial intelligence for competency assessment in engineering education: a review

Ndiaye,

Lim,

Blessing

2023

Front. Educ.

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In recent years, eye-tracking (ET) methods have gained an increasing interest in STEM education research. When applied to engineering education, ET is particularly relevant for understanding some aspects of student behavior, especially student competency, and its assessment. However, from the instructor’s perspective, little is known about how ET can be used to provide new insights into, and ease the process of, instructor assessment. Traditionally, engineering education is assessed through time-consuming and labor-extensive screening of their materials and learning outcomes. With regard to this, and coupled with, for instance, the subjective open-ended dimensions of engineering design, assessing competency has shown some limitations. To address such issues, alternative technologies such as artificial intelligence (AI), which has the potential to massively predict and repeat instructors’ tasks with higher accuracy, have been suggested. To date, little is known about the effects of combining AI and ET (AIET) techniques to gain new insights into the instructor’s perspective. We conducted a Review of engineering education over the last decade (2013–2022) to study the latest research focusing on this combination to improve engineering assessment. The Review was conducted in four databases (Web of Science, IEEE Xplore, EBSCOhost, and Google Scholar) and included specific terms associated with the topic of AIET in engineering education. The research identified two types of AIET applications that mostly focus on student learning: (1) eye-tracking devices that rely on AI to enhance the gaze-tracking process (improvement of technology), and (2) the use of AI to analyze, predict, and assess eye-tracking analytics (application of technology). We ended the Review by discussing future perspectives and potential contributions to the assessment of engineering learning.

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Section: Challenges Of Aietmentioning

confidence: 99%

Eye tracking and artificial intelligence for competency assessment in engineering education: a review

Ndiaye,

Lim,

Blessing

2023

Front. Educ.

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“…In (Morozkin, Swynghedauw, & Trocan, 2017, September) the EyeDee™ (Figure 2) embedded eye tracking solution developed by SuriCog was introduced. The actual problem is the deployment of computationally intensive image processing-based eye tracking algorithms on a resource-constrained embedded platform, based on an MCU (Microcontroller Unit) and an FPGA (Field-Programmable Gate Array).…”

Section: Eyedee™ Eye Tracking Solutionmentioning

confidence: 99%

Image compression in resource-constrained eye tracking devices

Morozkin

Swynghedauw²,

Trocan

2019

Journal of Information and Telecommunication

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Resource-constrained embedded devices, operating with images, are becoming increasingly common. Examples include remote low-power smart sensors, wireless sensor networks, autonomous cameras, eye tracking devices, etc. The principal requirements of such devices are the operation in real time, low power, low heat as well as low MIPS. These requirements can be fulfilled with the use of approximated version of original image processing algorithms. The EyeDee™ embedded eye tracking solution (developed by SuriCog) is the world's first innovative solution using the eye as a real-time mobile digital cursor, while maintaining full mobility. Being an example of resourceconstrained embedded device, the system consists of a wearable device (Weetsy™ frame) capturing images on the human's eye and an embedded pre-processing device (Weetsy™ preprocessing board) sending these eye images over a transmission medium (wire/wireless transmission) to a remote processing unit for the further gaze reconstruction. This paper is aimed at introducing image compression approaches in the resourceconstrained devices in general and some of their implementation in the Weetsy™ pre-processing board in particular.

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