Validating models of sensory conflict and perception for motion sickness prediction.

IF 1.7 4区 工程技术 Q3 COMPUTER SCIENCE, CYBERNETICS Biological Cybernetics Pub Date : 2023-06-01 DOI:10.1007/s00422-023-00959-8
Tugrul Irmak, Daan M Pool, Ksander N de Winkel, Riender Happee
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引用次数: 5

Abstract

The human motion perception system has long been linked to motion sickness through state estimation conflict terms. However, to date, the extent to which available perception models are able to predict motion sickness, or which of the employed perceptual mechanisms are of most relevance to sickness prediction, has not been studied. In this study, the subjective vertical model, the multi-sensory observer model and the probabilistic particle filter model were all validated for their ability to predict motion perception and sickness, across a large set of motion paradigms of varying complexity from literature. It was found that even though the models provided a good match for the perception paradigms studied, they could not be made to capture the full range of motion sickness observations. The resolution of the gravito-inertial ambiguity has been identified to require further attention, as key model parameters selected to match perception data did not optimally match motion sickness data. Two additional mechanisms that may enable better future predictive models of sickness have, however, been identified. Firstly, active estimation of the magnitude of gravity appears to be instrumental for predicting motion sickness induced by vertical accelerations. Secondly, the model analysis showed that the influence of the semicircular canals on the somatogravic effect may explain the differences in the dynamics observed for motion sickness induced by vertical and horizontal plane accelerations.

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对晕动病预测的感觉冲突和知觉模型的验证。
长期以来,人们通过状态估计冲突术语将人体运动感知系统与晕动病联系起来。然而,迄今为止,在何种程度上可用的感知模型能够预测晕动病,或采用的感知机制是最相关的疾病预测,尚未研究。在这项研究中,主观垂直模型、多感官观察者模型和概率粒子滤波模型都被验证了它们预测运动感知和眩晕的能力,这些模型跨越了文献中大量不同复杂性的运动范式。研究发现,尽管这些模型与所研究的感知范式很好地匹配,但它们无法捕捉到晕动病观察的全部范围。由于选择用于匹配感知数据的关键模型参数并没有最佳地匹配晕动病数据,因此需要进一步关注重力-惯性模糊的分辨率。然而,已经确定了另外两种可能使未来疾病预测模型更好的机制。首先,主动估计重力大小似乎有助于预测由垂直加速度引起的晕动病。其次,模型分析表明,半规管对体重效应的影响可能解释了垂直和水平平面加速度引起的晕动病的动力学差异。
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来源期刊
Biological Cybernetics
Biological Cybernetics 工程技术-计算机:控制论
CiteScore
3.50
自引率
5.30%
发文量
38
审稿时长
6-12 weeks
期刊介绍: Biological Cybernetics is an interdisciplinary medium for theoretical and application-oriented aspects of information processing in organisms, including sensory, motor, cognitive, and ecological phenomena. Topics covered include: mathematical modeling of biological systems; computational, theoretical or engineering studies with relevance for understanding biological information processing; and artificial implementation of biological information processing and self-organizing principles. Under the main aspects of performance and function of systems, emphasis is laid on communication between life sciences and technical/theoretical disciplines.
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