Validating models of sensory conflict and perception for motion sickness prediction

The operational development of diagnostic seasickness criteria through a human cyber-physical system

Thresholds that guide diagnoses of probable and acceptable seasickness levels on board ships are scarcely reported in literature. Motion sickness incidence and motion sickness dose value thresholds exist, but are defined for specific environments, such as naval, or offered merely as optional criteria for ship performance metrics. The presented work communicates a novel means of developing seasickness diagnostic criteria during ship operation, based on observations from shipboard measurement systems and seafarers using an innovative platform. The innovative platform provides personalised seasickness criteria that are accessible during ship operation to estimate the probable level of seasickness on board. Results are compared to that from a traditional method of data acquisition and analyses, post operation, revealing a similar trend in diagnostic threshold magnitudes (13-85 m/s1.5) that can be applicable to voyages with different durations (0.5-6 hr) considering desired levels of seasickness (10-50 %). The seasickness criteria are envisioned to be pertinent for the prediction of probable seasickness levels based on sea state forecasts and ship motion estimation.

A computational model of motion sickness dynamics during passive self-motion in the dark

Predicting the time course of motion sickness symptoms enables the evaluation of provocative stimuli and the development of countermeasures for reducing symptom severity. In pursuit of this goal, we present an Observer-driven model of motion sickness for passive motions in the dark. Constructed in two stages, this model predicts motion sickness symptoms by bridging sensory conflict (i.e., differences between actual and expected sensory signals) arising from the Observer model of spatial orientation perception (stage 1) to Oman's model of motion sickness symptom dynamics (stage 2; presented in 1982 and 1990) through a proposed "Normalized Innovation Squared" statistic. The model outputs the expected temporal development of human motion sickness symptom magnitudes (mapped to the Misery Scale) at a population level, due to arbitrary, 6-degree-of-freedom, self-motion stimuli. We trained model parameters using individual subject responses collected during fore-aft translations and off-vertical axis of rotation motions. Improving on prior efforts, we only used datasets with experimental conditions congruent with the perceptual stage (i.e., adequately provided passive motions without visual cues) to inform the model. We assessed model performance by predicting an unseen validation dataset, producing a Q2 value of 0.91. Demonstrating this model's broad applicability, we formulate predictions for a host of stimuli, including translations, earth-vertical rotations, and altered gravity, and we provide our implementation for other users. Finally, to guide future research efforts, we suggest how to rigorously advance this model (e.g., incorporating visual cues, active motion, responses to motion of different frequency, etc.).

The role of vision in sensory integration models for predicting motion perception and sickness

Users of automated vehicles will engage in other activities and take their eyes off the road, making them prone to motion sickness. To resolve this, the current paper validates models predicting sickness in response to motion and visual conditions. We validate published models of vestibular and visual sensory integration that have been used for predicting motion sickness through sensory conflict. We use naturalistic driving data and laboratory motion (and vection) paradigms, such as sinusoidal translation and rotation at different frequencies, Earth-Vertical Axis Rotation, Off-Vertical Axis Rotation, Centrifugation, Somatogravic Illusion, and Pseudo-Coriolis, to evaluate different models for both motion perception and motion sickness. We investigate the effects of visual motion perception in terms of rotational velocity (visual flow) and verticality. According to our findings, the SVCI model, a 6DOF model based on the Subjective Vertical Conflict (SVC) theory, with visual rotational velocity input is effective at estimating motion sickness. However, it does not correctly replicate motion perception in paradigms such as roll-tilt perception during centrifuge, pitch perception during somatogravic illusion, and pitch perception during pseudo-Coriolis motions. On the other hand, the Multi-Sensory Observer Model (MSOM) accurately models motion perception in all considered paradigms, but does not effectively capture the frequency sensitivity of motion sickness, and the effects of vision on sickness. For both models (SVCI and MSOM), the visual perception of rotational velocity strongly affects sickness and perception. Visual verticality perception does not (yet) contribute to sickness prediction, and contributes to perception prediction only for the somatogravic illusion. In conclusion, the SVCI model with visual rotation velocity feedback is the current preferred option to design vehicle control algorithms for motion sickness reduction, while the MSOM best predicts perception. A unified model that jointly captures perception and motion sickness remains to be developed.

Neck stabilization through sensory integration of vestibular and visual motion cues

BACKGROUND

To counteract gravity, trunk motion, and other perturbations, the human head-neck system requires continuous muscular stabilization. In this study, we combine a musculoskeletal neck model with models of sensory integration (SI) to unravel the role of vestibular, visual, and muscle sensory cues in head-neck stabilization and relate SI conflicts and postural instability to motion sickness.

METHOD

A 3D multisegment neck model with 258 Hill-type muscle elements was extended with postural stabilization using SI of vestibular (semicircular and otolith) and visual (rotation rate, verticality, and yaw) cues using the multisensory observer model (MSOM) and the subjective vertical conflict model (SVC). Dynamic head-neck stabilization was studied using empirical datasets, including 6D trunk perturbations and a 4 m/s2 slalom drive inducing motion sickness.

RESULTS

Recorded head translation and rotation are well matched when using all feedback loops with MSOM or SVC or assuming perfect perception. A basic version of the model, including muscle, but omitting vestibular and visual perception, shows that muscular feedback can stabilize the neck in all conditions. However, this model predicts excessive head rotations in conditions with trunk rotation and in the slalom. Adding feedback of head rotational velocity sensed by the semicircular canals effectively reduces head rotations at mid-frequencies. Realistic head rotations at low frequencies are obtained by adding vestibular and visual feedback of head rotation based on the MSOM or SVC model or assuming perfect perception. The MSOM with full vision well captures all conditions, whereas the MSOM excluding vision well captures all conditions without vision. The SVC provides two estimates of verticality, with a vestibular estimate SVCvest, which is highly effective in controlling head verticality, and an integrated vestibular/visual estimate SVCint which can complement SVCvest in conditions with vision. As expected, in the sickening drive, SI models imprecisely estimate verticality, resulting in sensory conflict and postural instability.

CONCLUSION

The results support the validity of SI models in postural stabilization, where both MSOM and SVC provide credible results. The results in the sickening drive show imprecise sensory integration to enlarge head motion. This uniquely links the sensory conflict theory and the postural instability theory in motion sickness causation.

A computational model of motion sickness dynamics during passive self-motion in the dark

Predicting the time course of motion sickness symptoms enables the evaluation of provocative stimuli and the development of countermeasures for reducing symptom severity. In pursuit of this goal, we present an observer-driven model of motion sickness for passive motions in the dark. Constructed in two stages, this model predicts motion sickness symptoms by bridging sensory conflict (i.e., differences between actual and expected sensory signals) arising from the observer model of spatial orientation perception (stage 1) to Oman's model of motion sickness symptom dynamics (stage 2; presented in 1982 and 1990) through a proposed "Normalized innovation squared" statistic. The model outputs the expected temporal development of human motion sickness symptom magnitudes (mapped to the Misery Scale) at a population level, due to arbitrary, 6-degree-of-freedom, self-motion stimuli. We trained model parameters using individual subject responses collected during fore-aft translations and off-vertical axis of rotation motions. Improving on prior efforts, we only used datasets with experimental conditions congruent with the perceptual stage (i.e., adequately provided passive motions without visual cues) to inform the model. We assessed model performance by predicting an unseen validation dataset, producing a Q2 value of 0.86. Demonstrating this model's broad applicability, we formulate predictions for a host of stimuli, including translations, earth-vertical rotations, and altered gravity, and we provide our implementation for other users. Finally, to guide future research efforts, we suggest how to rigorously advance this model (e.g., incorporating visual cues, active motion, responses to motion of different frequency, etc.).

Could Vulnerability to Motion Sickness and Chronic Pain Coexist within a Sensorimotor Phenotype? Insights from over 500 Pre-Pain Motion Sickness Reports

BACKGROUND

The sensorimotor incongruence theory proposes that certain instances of pain result from conflicts in the brain's sensorimotor networks. Indeed, injuries may cause abnormalities in afferent and cortical signaling resulting in such conflicts. Motion sickness also occurs in instances of incongruent sensorimotor data. It is possible that a sensory processing phenotype exists that predisposes people to both conditions.

AIM

The aim of this study was to investigate whether participants with chronic pain recall greater susceptibility to motion sickness before chronic pain onset.

METHOD

Data were collected via an online LimeSurvey. A self-report tendency toward motion sickness was measured using the Motion Sickness Susceptibility Questionnaire. Group differences were analysed using analysis of covariance methods.

RESULTS

530 patients (low back pain, n = 198; neck pain, n = 59; whiplash-associated disorder, n = 72; fibromyalgia syndrome, n = 114; Migraine, n = 41) and 165 pain-free controls were surveyed. ANCOVA analysis, using sex and anxiety as covariates, suggested that childhood motion sickness susceptibility scores differed by group (F = 2.55 (6, 615), p = 0.019, (ηp2) = 0.024). Planned comparisons, with corrected p-values, suggested that childhood motion sickness was not statistically greater for low back pain, rheumatoid arthritis, migraine, neck pain or whiplash-associated disorder (ps > 0.05), although scores were on average 27%, 42%, 47%, 48% and 58% higher, respectively. Childhood susceptibility was statistically higher in people with FMS (p = 0.018), with scores on average 83% higher than controls. ANCOVA analysis, using sex and anxiety as covariates, suggested that adult motion sickness susceptibility scores did not differ by group (F = 1.86 (6, 613), p = 0.086), although average scores were, on average, at least 33% higher in persistent pain groups.

CONCLUSIONS

According to retrospective reporting, greater susceptibility to motion sickness appears to pre-date persistent pain in some conditions. This supports the possibility that motion sickness and chronic pain may, in some cases, have overlapping mechanisms related to the handling of incongruent sensorimotor data.