Mitigating motion sickness with non-invasive cortical vestibular brain stimulation (vBS) is promising because it does not require redesigning VR content or altering the motion profile of a moving platform. Instead, it aims to enhance individuals' intrinsic resistance to motion sickness. However, the mechanism underlying its online efficacy remains unclear (Here, "online efficacy" means the observed motion sickness reduction during vBS not before or after vBS). Motivated by lower motion sickness susceptibility observed in congenital nystagmus patients, we hypothesized that reductions in motion sickness under vBS condition are associated with increased eye movement instability. Leveraging sham-controlled, sex-balanced eye-tracking data from an open-access database, this study presents the first evaluation of a 20 Hz-vBS approach targeting the vestibular cortices as a countermeasure to motion sickness, using spatiotemporal eye movement metrics derived from eye-tracking data collected in VR. Results show that reduced motion sickness under active vBS indeed increased eye movement instability temporarily and spatially, compared to sham vBS. We also found that the temporal metrics was more strongly associated with female participants, whereas the spatial metric was more strongly associated with male participants.

The increasing use of virtual reality (VR) in public transportation enables travelers to engage in immersive entertainment or productive tasks, thereby enhancing the overall travel experience. However, mismatched VR visual motion and physical car motion can cause motion sickness (MS), leading to nausea, postural instability and reduced time for enjoyment or productive tasks. Thus, the benefits of using VR in transportation systems are currently limited to individuals who do not experience MS. Thus, effective MS mitigation is essential for improving travel safety, maximizing travel time, and expanding VR accessibility. Neuromodulation targeting the vestibular apparatus, such as bone-conducted vibration (BCV), has shown promise in reducing MS. However, it remains unclear whether neuromodulation directly targeting vestibular cortical regions, such as transcranial alternating current stimulation (tACS), is superior. This paper focuses on the design and validation of a novel vestibular cortical neuromodulation approach using tACS in mitigating MS in a novel simulated in-car VR environment. Eighty participants were recruited to evaluate the tACS approach. The results demonstrate that the proposed tACS approach effectively reduces nausea, enhances postural stability, and extends survival time in MS. Compared to BCV, tACS demonstrates a faster onset of action and longer mitigation effects. However, from an applied perspective, the effects of our tACS approach were relatively short-lived and accompanied by side effects such as tingling and itching.

This paper presents the first in-car VR motion sickness (VRMS) detection model based on lower face action units (LF-AUs). Initially developed in a simulated in-car environment with 78 participants, the model’s generalizability was later tested in realworld driving conditions. Motion sickness was induced using visual linear motion in the VR headset and physical horizontal rotation via a rotating chair. We used a convolutional neural network (MobileNetV3) to automatically extract LF-AUs from images of the users’ mouth region, captured by the VR headset’s built-in camera. These LF-AUs were then used to train a Support Vector Regression (SVR) model to estimate motion sickness scores. We compared the SVR model’s performance using LF-AUs, pupil diameters, and physiological features (individually and in combination) from the same VR headset. Results showed that both individual LF-AU (right dimple) and combined LF-AUs had significant Pearson correlations with self-reported motion sickness scores and achieved lower root mean squared error compared to pupil diameters. The best detection results were obtained by combining LF-AUs and pupil diameters, while physiological features alone did not yield significant results. The LF-AUs-based model demonstrated encouraging generalizability across different settings in the independent studies.

Though computer systems have entered widespread use for animals' enrichment in zoos, no interactive computer systems suited to giraffes have yet been developed. Hence, which input modes or audio stimuli giraffes might best utilise remains unknown. To address this issue and probe development of such systems alongside the animals themselves and zookeepers, researchers gathered requirements from the keepers and from prototyping with giraffes, then created two interfaces -- one touch-based and one proximity-based -- that play giraffe-humming audio or white noise when activated. Over two months of observation, giraffes utilised the proximity-based system more frequently than the touch-based one but in shorter episodes. Secondly, the study highlighted the significance of considering user-specific needs in computer systems' development: the lack of preference shown for any specific audio type indicates that the audio stimuli chosen were inappropriate for these giraffes. In addition, the paper articulates several lessons that can be drawn from human--computer interaction when one develops systems for animals and, in turn, what the findings presented mean for humans.