Development of Concussion Evaluation Tools Using Life-Like Virtual Reality Environments

Sawires, Yousef; Huang, Elaine M.; Gomes, Adam; Fernandes, Keegan; Wang, David

doi:10.1007/978-3-319-92279-9_44

Cited by 1 publication

(1 citation statement)

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“…In recent years, VR has been recognized as a valuable tool, both as a stimulus and as a measurement tool offering new ways to study psychological and behavioral factors related to health 123 , even showing promise in detecting and treating Alzheimer´s dementia 124 . In one study, EMG measures were acquired in a VR environment among high-risk concussion participants 55 . The experiment was a preliminary study to verify data in a VR environment.…”

Section: Introductionmentioning

confidence: 99%

Towards defining biomarkers to evaluate concussions using virtual reality and a moving platform (BioVRSea)

Jacob

Kristensen

Aubonnet

et al. 2022

Sci Rep

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Current diagnosis of concussion relies on self-reported symptoms and medical records rather than objective biomarkers. This work uses a novel measurement setup called BioVRSea to quantify concussion status. The paradigm is based on brain and muscle signals (EEG, EMG), heart rate and center of pressure (CoP) measurements during a postural control task triggered by a moving platform and a virtual reality environment. Measurements were performed on 54 professional athletes who self-reported their history of concussion or non-concussion. Both groups completed a concussion symptom scale (SCAT5) before the measurement. We analyzed biosignals and CoP parameters before and after the platform movements, to compare the net response of individual postural control. The results showed that BioVRSea discriminated between the concussion and non-concussion groups. Particularly, EEG power spectral density in delta and theta bands showed significant changes in the concussion group and right soleus median frequency from the EMG signal differentiated concussed individuals with balance problems from the other groups. Anterior–posterior CoP frequency-based parameters discriminated concussed individuals with balance problems. Finally, we used machine learning to classify concussion and non-concussion, demonstrating that combining SCAT5 and BioVRSea parameters gives an accuracy up to 95.5%. This study is a step towards quantitative assessment of concussion.

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Section: Introductionmentioning

confidence: 99%