Name
Title | ||
|---|---|---|
Machine learning assessment of visually induced motion sickness levels based on multiple biosignals |
Abstract | ||
|---|---|---|
•Multiple levels of motion sickness are evaluated.•The data of all the subjects are mixed, which increases the difficulty.•Three different kinds of signals are fused and different combinations are discussed.•The voting classifier performs best in all assessment tasks.•The application areas include VR devices and training process. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.bspc.2018.12.007 | Biomedical Signal Processing and Control |
Keywords | Field | DocType |
Motion sickness level,Voting,Classifier,EEG,Information fusion | Kappa,Binary classification,Pattern recognition,Motion sickness,Center of pressure (fluid mechanics),Artificial intelligence,Classifier (linguistics),Random forest,Logistic regression,Electroencephalography,Mathematics | Journal |
Volume | ISSN | Citations |
49 | 1746-8094 | 4 |
PageRank | References | Authors |
0.44 | 0 | 3 |