Toward Predicting Motion Sickness Using Virtual Reality and a Moving Platform Assessing Brain, Muscles, and Heart Signals - Recenti, Marco; Ricciardi, Carlo; Aubonnet, Romain; Picone, Ilaria; Jacob, Deborah; Svansson, Halldór Á. R.; Agnarsdóttir, Sólveig; Karlsson, Gunnar H.; Baeringsdóttir, Valdís; Petersen, Hannes; Gargiulo, Paolo (Frontiers in Bioengineering and Biotechnology, 2021)
Motion sickness (MS) and postural control (PC) conditions are common complaints among those who passively travel. Many theories explaining a probable cause for MS have been proposed but the most prominent is the sensory conflict theory, stating that a mismatch between vestibular and visual signals causes MS. Few measurements have been made to understand and quantify the interplay between muscle activation, brain activity and heart behavior during this condition. We introduce here a novel multimetric system called BioVRSea based on virtual reality (VR), a mechanical platform and several biomedical sensors to study the physiology associated with MS and seasickness. This study reports the results from 28 individuals: the subjects stand on the platform wearing VR goggles, a 64-channel EEG dry-electrode cap, 2 EMG sensors on the gastrocnemius muscles and a sensor on the chest that captures the heart rate (HR). The virtual environment shows a boat surrounded by waves whose frequency and amplitude are synchronized with the platform movement. Three measurement protocols are performed by each subject after each of which, they answer the Motion Sickness Susceptibility Questionnaire. 19 parameters are extracted from the biomedical sensors (5 from EEG, 12 from EMG and two from HR) and 13 from the questionnaire. Eight binary indexes are computed to quantify the symptoms combining all of them in the Motion Sickness Index (IMS). These parameters create the MS database composed of 83 measurements. All indexes undergo univariate statistical analysis, EMG parameters being most significant, in contrast to EEG parameters. Machine learning (ML) gives good results in the classification of the binary indexes, finding random forest to be the best algorithm (accuracy of 74.7 for IMS). The feature importance analysis showed that muscle parameters are the most relevant and for EEG analysis, beta wave results were most important. The present work serves as first step in identifying the key physiological factors that differentiate those who suffer from MS from those who do not using the novel BioVRSea system. Coupled with ML, BioVRSea is of value in evaluation of PC disruptions which are among the most disturbing and costly health conditions affecting humans.