Measuring visually induced motion sickness using wearable devices

Visually induced motion sickness (VIMS) is frequently reported with stereo and VR display systems. We tested whether VIMS can be detected by off-the-shelve wearable electrophysiology devices, where the VIMS were induced by driving in the virtual world. Our data indicates that 1) the correlation between blood pressure and heart rate, and 2) the changes of mean gravity frequencies in TP9Delta and FP1Theta, and 3) the changes of SDs in TP9Alpha and TP10Alpha of the EEG signals may be possible candidates of the VIMS onset indicator. However, it is still hard to conclude that those physiological signals can be used as definitive VIMS indicators because our analysis only differentiates the physiological response to VIMS vs. non-VIMS, not the detection of VIMS onset, nor estimation of VIMS severity in real-time. Introduction Visually induced motion sickness (VIMS) is a discomfort disorder, which is often induced when a person is exposed to virtual and physical motions that are not well matched. For example, when a person immerses into a virtual environment of a driving simulator, the optic flow of the driving scene induces strong vection (self-motion illusion), but the vestibular system and the skeletomuscular system generates no self-motion because the person who drives is usually stationary in the static driver seat. Mismatched motion signals are not limited to inter-sensory conflicts but may affect intra-sensory signal conflicts, such as dynamic spatiotemporal distortions of expected stability of rigid world [1]. A person experiencing VIMS suffers headaches, dizziness, disorientation, stomach awareness, nausea, and even vomiting. This phenomenon raises safety and health concerns with current virtual reality (VR) platforms, in particular, head mounted display (HMD) systems. The VIMS has been identified as a major hurdle to overcome for wider spread use of the VR systems. Although many VR HMDs incorporate motion trackers to synchronize the users’ virtual view with physical motions, VIMS still occurs frequently, presumably because fully synchronized view is difficult to achieve. Preand postVIMS questionnaires [2] developed for flight simulators have been used as the main method to measure the presence and level of the VIMS experienced due to the virtual environment exposure. However, this subjective measure of the symptoms may strongly prime the subject, and only get coarse temporal changes of the VIMS. In other words, it generally measures aftereffects of the VIMS. To investigate causes of the VIMS or develop a VIMS countermeasure, it is essential to have VIMS measuring methods that (objectively) quantify the magnitude of VIMS. Physiological measures supporting the objective and frequent (quasi-continuous) measure, such as electrogastrography (EGG), electrocardiography (ECG), salivary cortisol level, blood pressure (BP), heart rate (HR), and electroencephalography (EEG) have been proposed and tested as possible objective markers of VIMS to overcome the limitations of the subjective VIMS rating. Cheung et al. [3] reviewed more than 10 years of EGG studies connecting gastric activity with changes in VIMS level, and concluded that EGG was not a reliable measure for VIMS because the increase in gastric activity from 1cpm to 3cpm is not always presented in VIMS onset. However, Kim et al. [4] found that net tachygastria does increase with VIMS, and argue that the EGG can be used as a measure of the VIMS. Ujike, et al. [5] computed the ratio of power between lower frequency (LF: 0.05-0.1 Hz) and high frequency (HF: 0.15-0.4 Hz) of the ECG, which is an index of sympathetic nerve activity, for 2D and 3D stimulus viewing groups, and they found an overall increase of LF/HF ratio in 3D compared to 2D, which matched well with the VIMS estimated by questionnaire. Similar results were reproduced by Kiryu, et al. [6]. Ramsey [7] measured heart rate (HR) and salivary cortisol level before and after exposing to virtual reality environment, and found an increase of both HR and cortisol level, and they correlate well with subjective VIMS questionnaire scores. A study by Graybiel et al. [8] found that BP nor HR changes did not indicate VIMS level changes. However, Holmes et al. [9] reported that BP and HR variability are well correlated with the level of VIMS. The changes in BP and HR variability were also found by Yang, et al. [10], when subjects viewed 2D and S3D movies. Changes were larger for the 3D condition. Sugita et al. [11] observed BP and HR changes during the VIMS stimulus exposure in VIMS-onset and non-VIMS-onset groups, and found that the temporal change of BP was less correlated to HR changes in VIMS onset group, suggesting that BP and HR correlation may be used as an objective VIMS measure. A similar conclusion was derived by Abe et al. [12] by computing the maximum cross-correlation coefficient between BP and HR variability using a finger photoplethysmography (PPG). Electroencephalograph (EEG) was also tested as a surrogate measure of VIMS by Lin, et al. [13], Chen, et al. [14], and Naqvi, et al. [15]. All suggested that the alpha and gamma bands of the EEG power spectrum are valid indicators of VIMS, where a decrease in alpha band power represents a VIMS onset signature. 218 IS&T International Symposium on Electronic Imaging 2017 Human Vision and Electronic Imaging 2017 https://doi.org/10.2352/ISSN.2470-1173.2017. 14.HVEI-147 © 2017, Society for Imaging Science and Technology