Jaromir Vogt

Advisors

Wolfgang Mehringer (M. Sc.), Maike Stöve (M. Sc.), Prof. Dr. Björn Eskofier, Prof. Dr. Georg Michelson

Duration

01 / 2023 – 07 / 2023

Abstract

With annual incidence rates reaching 600 per 100.000 and the risk of more critical injuries if undetected, mild traumatic brain injuries (mTBI) pose a sanitary threat, especially for athletes and elderly people [1]. A concussion is defined as a change in brain function that occurs after a force to the head, which may be accompanied by temporary loss of consciousness but is detected in awake individuals using neurologic and cognitive dysfunction measures [2]. These measures are based on computer tomography (CT) scans in hospitals or on the patient’s subjective self-reported symptoms [1, 3].

Especially in sports when a CT scan is not feasible on the sideline the concussion assessment still relies on standardized but subjective assessment tools, hence, a timely diagnosis of concussion remains difficult [4, 5, 6]. Players who continue to play after suffering a head injury have a higher risk of developing persistent post-trauma syndrome (PTS) and life-threatening states, such as second impact syndrome and the possibility of later accelerated neurodegeneration [1, 7]. Hence, there is an urgent need to identify and validate objective diagnostic methods that can assist physicians and doctors in the assessment of head injuries.

Current approaches can be divided into three main categories [6]: cognitive, motor, and visual based tools. The cognitive tools are common to be digital representations of the aforementioned subjective assessment tools. To increase accuracy, tools often rely on baseline measurements which complicates the use. The motor tools are based on close observation or on inertial measurement units to capture movements. Correlation between this data and concussion remains unclear, so further research has to be done. The visual tools rely on eye-tracking either by observation or by eye-tracking devices to analyze the saccadic eye movements. It was shown that when mTBI individuals did saccadic eye movement tasks, they had longer latencies than Control participants [8]. However, there is still a lack of meaningful thresholds or measures to allow the determination of mTBIs based on eye-tracking data alone [6]. Nevertheless, it is well known that concussions can lead to focal neurological deficits in which the eyes cannot jointly fixate on a target [1]. Because of that, a simple stereoscopic task can help to identify those deficits and has proven to be a good assessment tool for concussions [9].

This thesis is based on the work of Mehringer et. al. [10], which uses a virtual reality (VR)-adapted version of the Wirt-Circles as its stereoscopic task and proposes a classification pipeline to distinguish controls from Post-COVID patients [11]. For this thesis, we want to enhance the gaze behavior data and the stereopsis performance data from the original work with additional eye-tracking data, like scanpath data or fixation behaviour data, to test for focal neurological deficits [12].

Previous approaches have their problems in partly high license fees for eye-tracking solutions or insufficient portability to be able to use them on the sidelines or at home [13, 14, 15]. With the general goal of creating a reliable, affordable concussion assessment tool, this work tries to address the problems and provide an extended data basis for further analysis. Therefore, we intend to improve the VR concussion assessment tool VR-OTS in terms of its user experience (UX) and make it available on mobile devices. For this purpose, the application will first be optimized with regard to its user-friendliness, which will be evaluated in the subsequent study utilizing common UX questionnaires, like UEQ or SUS [16, 17]. The test setup remains the same as before. In the scene, there will be four balls as stimuli appearing in sequence at nine gaze points: upper, lower, left, right, middle, upper left, lower left, upper right, and lower right. One of the four balls appears to be closer to the user which has to be identified by user input. Primary data acquired during this task are the correctness of the task (whether the correct ball was identified) and the reaction time (time from stimulus presentation and button press). In addition the search and movement behavior of the eyes is tracked during the examination. The time of a fixation on a particular ball and the switching behavior of the focus are of interest. We expect a difference in accuracy, reaction time, and eye behavior when comparing healthy controls and concussion patients. By combining several objective measurements, different visual modalities can be covered to establish a fast and reliable method for the identification of mTBIs.

References:
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[2] NCAA Sport Science Institute.: 2014-2015 NCAA Sports Medicine Handbook. National Collegiate Athletic Association, Indianapolis, IN..
[3] Robert Graham. et al.: Sports-Related Concussions in Yough: Improving the Science, Changing the Culture. 2013.
[4] Paul McCrory. et al.: Consensus statement on concussion in sportsport-the 5th international conference on concussion in sport held in Berlin. October 2016. British Journal of Sports Medicine 51, 11 (2017),838-847. https://doi.org/10.1136/bjsports-2017-097699
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[10] Mehringer et al.: Virtual Reality for Assessing Stereoscopic Performance and Eye Characteristics in Post-COVID 2022
[11] Hunfalvary et al.: Evaluation of Stereo Acuity in Professional Baseball and LPGA Athletes Compared to Non-Athletes 2017
[12] Drusch et al.: Analysing Eye-Tracking Data: From Scanpaths and Heatmaps to the Dynamic Visualisation of Areas of Interest 2020
[13] Schrader et al.: Toward Eye-Tracked Sideline Concussion Assessment in eXtended Reality 2021
[14] Yue et al.: Sideline concussion assessment: The current state of the art 2020
[15] Maruta et al.: Association of visual tracking metrics with Post-Concussion Symptomatology 2018
[16] Laugwitz et al.: Construction and evaluation of a user experience questionnaire 2008
[17] J. Brooke: SUS: A ’Quick and Dirty’ Usability Scale 1995