Our Blog
Every eye movement
tells a story
For many years fatigue has been a topic of scientific and applied research, especially in relation to evaluating a person’s cognitive state in occupations settings such as factory work, client services, education and other areas where a work routine tends to lead to fatigue. Reading and education are also topics of great scientific interest in their broader sense. A person who is tired is hardly capable of perceiving new information; tiredness reduces cognitive abilities including attention, concentration and a capacity for work. A reader becomes distracted when fatigue develops.
In this study the authors developed algorithms for estimating fatigue during reading based on eye tracking data collected from modern smartphones or tablet PCs. The described method could prove useful for evaluating reading and comprehension effectiveness in order to make predictions about the state of a person’s cognitive capabilities.
Arsen Revazov, Victor Anisimov, Ksenia Babanova, Daria Zhigulskaya, Andrey Pikunov, Sergey Zuev, Alexandra Latyshkova, Konstantin Chernozatonskiy
Neurophysiology in OkenTech: which parameters formed the basis of the company’s research
What is the best parameter to assess the state of a reading person – using only their smartphone or tablet? Naturally, eye movements. They can be recorded in a fully non-contact way through the frontal camera of the device, and yet their features can provide objective information on the reader’s state – their engagement with the text, attention span or exhaustion levels. However, the psycho-emotional state of a person is a complex concept that is incredibly individual and requires delicacy and precision. Due to this we at OkenTech did not confine ourselves to simply tracking eye-movements during our research, but instead came up with a whole system using high-precision equipment to record and measure heart rate variability, muscle tension and even brain activity. In addition to this, we carefully selected texts of diametrically opposite characteristics to determine the possibility of distinguishing the degrees of attention, comprehension, interest and emotional response using only physiological parameters. Turns out, is it possible to determine these using only eye movements! You can read more about it in our paper published in the Procedia Computer Science journal in 2021.
Anisimov, V., Сhernozatonsky, K., Pikunov, A., Raykhrud, M., Revazov, A., Shedenko, K., Zhigulskaya D. & Zuev, S. (2021). OkenReader: ML-based classification of the reading patterns using an Apple iPad. Procedia Computer Science, 192, 1944-1953
Why use Machine Learning
in quality assessment
of reading?
Machine Learning is the future – it can process data of any size and detect patterns almost instantly, while a person can take days, weeks and even months to do the same task. Oken Reader technology incorporates ML-algorithms utilising data of a large number of readers who took part in our company’s scientific research. This was made for every OkenReader app user to receive detailed, exhaustive and correct analysis of their reading in just a few minutes, and to be able to immediately apply it to enhance their work with the text. This is particularly important in the field of education, which we consider one of the most high-priority spheres for applying this technology. Successful application of Machine Learning in the company’s technology became the basis for a scientific paper by our research team as a result of the International Conference on Cyberworlds.
Anisimov, V., Chernozatonsky, K., Pikunov, A., Shedenko, K., Zhigulskaya, D., & Arsen, R. (2021, September). ML-based classification of eye movement patterns during reading using eye tracking data from an Apple iPad device: Perspective machine learning algorithm needed for reading quality analytics app on an iPad with built-in eye tracking. In 2021 International Conference on Cyberworlds (CW) (pp. 188-193). IEEE.
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