Ordinal Distance-based Domain Adaptation Framework for Motion Sickness Classification

Han, Sang-In; Han, Dong-Kyun; Lee, Seong–Whan

doi:10.1109/smc53654.2022.9945427

2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC) 2022

DOI: 10.1109/smc53654.2022.9945427

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Ordinal Distance-based Domain Adaptation Framework for Motion Sickness Classification

Sang-In Han

Dong-Kyun Han

Seong–Whan Lee

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2024

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Cited by 2 publications

References 23 publications

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Significance of Physiological Signal Thresholds in the Early Diagnosis of Simulator Sickness

Rahimzadeh,

Pławiak,

Mohamed

et al. 2024

IEEE Access

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Significance of Physiological Signal Thresholds in the Early Diagnosis of Simulator Sickness

Rahimzadeh,

Pławiak,

Mohamed

et al. 2024

IEEE Access

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Domain Adaptation and Generalization of Functional Medical Data: A Systematic Survey of Brain Data

Sarafraz,

Behnamnia,

Hosseinzadeh

et al. 2024

ACM Comput. Surv.

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In spite of the excellent capabilities of machine learning algorithms, their performance deteriorates when the distribution of test data differs from the distribution of training data. In medical data research, this problem is exacerbated by its connection to human health, expensive equipment, and meticulous setups. Consequently, achieving domain generalizations (DG) and domain adaptations (DA) under distribution shifts is an essential step in the analysis of medical data. As the first systematic review of DG and DA on functional brain signals, the paper discusses and categorizes various methods, tasks, and datasets in this field. Moreover, it discusses relevant directions for future research.

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Ordinal Distance-based Domain Adaptation Framework for Motion Sickness Classification

Cited by 2 publications

References 23 publications

Significance of Physiological Signal Thresholds in the Early Diagnosis of Simulator Sickness

Significance of Physiological Signal Thresholds in the Early Diagnosis of Simulator Sickness

Domain Adaptation and Generalization of Functional Medical Data: A Systematic Survey of Brain Data

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