Irmak Tugrul, Kotian Varun, Happee Riender, de Winkel Ksander N, Pool Daan M
Cognitive Robotics Department, Delft University of Technology, Delft, Netherlands.
Control and Simulation Department, Delft University of Technology, Delft, Netherlands.
Front Syst Neurosci. 2022 May 9;16:866503. doi: 10.3389/fnsys.2022.866503. eCollection 2022.
The relationship between the amplitude of motion and the accumulation of motion sickness in time is unclear. Here, we investigated this relationship at the individual and group level. Seventeen participants were exposed to four oscillatory motion stimuli, in four separate sessions, separated by at least 1 week to prevent habituation. Motion amplitude was varied between sessions at either 1, 1.5, 2, or 2.5 ms. Time evolution was evaluated within sessions applying: an initial motion phase for up to 60 min, a 10-min rest, a second motion phase up to 30 min to quantify hypersensitivity and lastly, a 5-min rest. At both the individual and the group level, motion sickness severity (MISC) increased linearly with respect to acceleration amplitude. To analyze the evolution of sickness over time, we evaluated three variations of the Oman model of nausea. We found that the slow (502 s) and fast (66.2 s) time constants of motion sickness were independent of motion amplitude, but varied considerably between individuals (slow STD = 838 s; fast STD = 79.4 s). We also found that the Oman model with output scaling following a power law with an exponent of 0.4 described our data much better as compared to the exponent of 2 proposed by Oman. Lastly, we showed that the sickness forecasting accuracy of the Oman model depended significantly on whether the participants had divergent or convergent sickness dynamics. These findings have methodological implications for pre-experiment participant screening, as well as online tuning of automated vehicle algorithms based on sickness susceptibility.
运动幅度与晕动病随时间积累之间的关系尚不清楚。在此,我们在个体和群体层面研究了这种关系。17名参与者在四个单独的时间段内接受了四种振荡运动刺激,各时间段之间至少间隔1周以防止产生适应性。各时间段之间的运动幅度有所不同,分别为1、1.5、2或2.5毫秒。在各时间段内评估时间演变,具体如下:初始运动阶段持续60分钟,休息10分钟,第二个运动阶段持续30分钟以量化超敏反应,最后休息5分钟。在个体和群体层面,晕动病严重程度(MISC)均随加速度幅度呈线性增加。为了分析晕动病随时间的演变,我们评估了阿曼恶心模型的三种变体。我们发现,晕动病的慢时间常数(502秒)和快时间常数(66.2秒)与运动幅度无关,但个体之间差异很大(慢时间常数标准差 = 838秒;快时间常数标准差 = 79.4秒)。我们还发现,与阿曼提出的指数为2的情况相比,输出按指数为0.4的幂律缩放的阿曼模型对我们的数据拟合得更好。最后,我们表明,阿曼模型的晕动病预测准确性很大程度上取决于参与者的晕动病动态是发散还是收敛。这些发现对实验前参与者筛选以及基于晕动病易感性的自动驾驶车辆算法在线调整具有方法学意义。