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Equilibrium and Balance

Equilibrium and Balance

The inner ear assumes dual functionalities of auditory perception and equilibrium maintenance. The vestibule is the organ responsible for balance. This organ contains mechanoreceptors, specifically hair cells, endowed with stereocilia, which aid in deciphering information regarding the position and motion of our heads. Two intrinsic components, the utricle and saccule, help perceive head position, while the semicircular canals track head movement. Neurological messages initiated in the...

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Driving Simulation in the Clinic: Testing Visual Exploratory Behavior in Daily Life Activities in Patients with Visual Field Defects

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Exploring Eye Movement Features of Motion Sickness Using Closed-Track Driving Experiments.

Yanlu Cao¹, Maosong Jiang¹, Zhuxi Yao²

¹Dalian University of Technology, China.

|December 10, 2024

Summary

Eye movements effectively detect and predict motion sickness (MS). This study validated eye movement features and used an MLSTM-FCN model, achieving high accuracy in identifying and forecasting MS for practical applications.

Keywords:

eye movements motion sickness multivariate long short-term memory fully convolutional network

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Area of Science:

Neuroscience
Human-Computer Interaction
Automotive Engineering

Background:

Motion sickness (MS) is a growing concern with autonomous vehicle (AV) development.
Eye movements offer a non-invasive, objective indicator of vestibular function and MS severity.
Eye tracking is highly applicable due to ease of collection without passenger discomfort.

Purpose of the Study:

To identify and validate effective eye movement features for motion sickness (MS) detection.
To provide insights for practical applications in monitoring and early warning systems.
To assess the efficacy of a machine learning model for MS analysis.

Main Methods:

Collected eye movement data from 72 MS-susceptible participants in closed-track driving.
Extracted features: blink rate, fixation counts/durations, saccade metrics, and nystagmus.
Utilized statistical analysis and a multivariate long short-term memory fully convolutional network (MLSTM-FCN) for validation.

Main Results:

Statistical analysis revealed significant differences in eye movement features across MS levels.
MLSTM-FCN achieved 91.37% accuracy for binary MS detection and 88.51% for prediction.
Ternary classification yielded 80.54% accuracy for detection and 80.11% for prediction.

Conclusions:

Eye movement analysis is a validated and effective method for evaluating motion sickness (MS).
The MLSTM-FCN model demonstrates high efficiency in detecting and predicting MS using eye movement data.
This research offers a potential tool for early MS indication and warnings.