Multisensory Interactions in Virtual Reality: Optic Flow Reduces Vestibular Sensitivity, but Only for Congruent Planes of Motion.

During exposure to Virtual Reality (VR) a sensory conflict may be present, whereby the visual system signals that the user is moving in a certain direction with a certain acceleration, while the vestibular system signals that the user is stationary. In order to reduce this conflict, the brain may down-weight vestibular signals, which may in turn affect vestibular contributions to self-motion perception. Here we investigated whether vestibular perceptual sensitivity is affected by VR exposure. Participants' ability to detect artificial vestibular inputs was measured during optic flow or random motion stimuli on a VR head-mounted display. Sensitivity to vestibular signals was significantly reduced when optic flow stimuli were presented, but importantly this was only the case when both visual and vestibular cues conveyed information on the same plane of self-motion. Our results suggest that the brain dynamically adjusts the weight given to incoming sensory cues for self-motion in VR; however this is dependent on the congruency of visual and vestibular cues.

Improved Correction of Atmospheric Pressure Data Obtained by Smartphones through Machine Learning.

A correction method using machine learning aims to improve the conventional linear regression (LR) based method for correction of atmospheric pressure data obtained by smartphones. The method proposed in this study conducts clustering and regression analysis with time domain classification. Data obtained in Gyeonggi-do, one of the most populous provinces in South Korea surrounding Seoul with the size of 10,000 km(2), from July 2014 through December 2014, using smartphones were classified with respect to time of day (daytime or nighttime) as well as day of the week (weekday or weekend) and the user's mobility, prior to the expectation-maximization (EM) clustering. Subsequently, the results were analyzed for comparison by applying machine learning methods such as multilayer perceptron (MLP) and support vector regression (SVR). The results showed a mean absolute error (MAE) 26% lower on average when regression analysis was performed through EM clustering compared to that obtained without EM clustering. For machine learning methods, the MAE for SVR was around 31% lower for LR and about 19% lower for MLP. It is concluded that pressure data from smartphones are as good as the ones from national automatic weather station (AWS) network.