{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T12:14:37Z","timestamp":1785413677170,"version":"3.56.0"},"reference-count":30,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T00:00:00Z","timestamp":1785110400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"crossref","award":["2020R1A2C2013256"],"award-info":[{"award-number":["2020R1A2C2013256"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"crossref"}]},{"award":["2020R1A2C2013256"],"award-info":[{"award-number":["2020R1A2C2013256"]}],"id":[{"id":"https:\/\/ror.org\/013aysd81","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Motion sickness is expected to become more prevalent in road transportation as passengers increasingly engage in non-driving-related activities under automated driving. This study quantified the relative contributions of exposure time (discrete trial index), physiological signals, and vibration-derived features to motion sickness prediction under controlled road vibration. Thirty-nine participants were exposed to four road profiles reproduced on a motion simulator while electrocardiography (ECG), galvanic skin response (GSR), triaxial head acceleration, and motion sickness ratings were recorded across ten 2 min trials per session during a reading task. Three dataset configurations were compared: exposure time only (type 1), physiological and vibration-derived features only (type 2), and the combined (type 3) dataset. Motion sickness prediction was evaluated using support vector classification, random forest, and XGBoost, and permutation importance was used to assess feature contribution. Across all classifiers, exposure time was ranked as the most important feature, followed by overall root-mean-square, while ECG and GSR contributed at lower levels. The combined dataset achieved the best performance, reaching 77% classification accuracy and R2 values up to 0.49. Leave-one-subject-out (LOSO) cross-validation further confirmed the feature importance ranking under subject-level evaluation, while physiological features showed near-zero importance for unseen participants, suggesting their contributions are largely individual-specific.<\/jats:p>","DOI":"10.3390\/s26154774","type":"journal-article","created":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T14:08:14Z","timestamp":1785161294000},"page":"4774","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Quantifying the Relative Contributions of Exposure Time, ECG, GSR, and Vibration-Derived Features for Motion Sickness Prediction"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-9053-983X","authenticated-orcid":false,"given":"Abeeb Opeyemi","family":"Alabi","sequence":"first","affiliation":[{"name":"Department of Mechanical Engineering, Kyungpook National University, Daegu 41566, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Do-Kyung","family":"Kwak","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Kyungpook National University, Daegu 41566, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-8054-4396","authenticated-orcid":false,"given":"Byoung-Gyu","family":"Song","sequence":"additional","affiliation":[{"name":"Cognitive Science Research Group, Korea Brain Research Institute (KBRI), Daegu 41062, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Namcheol","family":"Kang","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Kyungpook National University, Daegu 41566, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,7,27]]},"reference":[{"key":"ref_1","unstructured":"Reason, J.T., and Brand, J.J. (1975). Motion Sickness, Academic Press."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"537","DOI":"10.1016\/S0361-9230(98)00088-4","article-title":"Modelling motion sickness and subjective vertical mismatch detailed for vertical motions","volume":"47","author":"Bos","year":"1998","journal-title":"Brain Res. Bull."},{"key":"ref_3","unstructured":"Griffin, M.J. (2012). Handbook of Human Vibration, Academic Press."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"867","DOI":"10.1080\/00140139108964831","article-title":"Why is the driver rarely motion sick? The role of controllability in motion sickness","volume":"34","author":"Rolnick","year":"1991","journal-title":"Ergonomics"},{"key":"ref_5","first-page":"1111","article-title":"Motion sickness symptoms in a ship motion simulator: Effects of inside, outside, and no view","volume":"76","author":"Bos","year":"2005","journal-title":"Aviat. Space Environ. Med."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"515","DOI":"10.1007\/s00221-020-05986-6","article-title":"Objective and subjective responses to motion sickness: The group and the individual","volume":"239","author":"Irmak","year":"2021","journal-title":"Exp. Brain Res."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"280","DOI":"10.1080\/00016480510003192","article-title":"Motion sickness susceptibility associated with visually induced postural instability and cardiac autonomic responses in healthy subjects","volume":"125","author":"Yokota","year":"2005","journal-title":"Acta Otolaryngol."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Kim, J., Napadow, V., Kuo, B., and Barbieri, R. (September, January 30). A combined HRV-fMRI approach to assess cortical control of cardiovagal modulation by motion sickness. Proceedings of the 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Boston, MA, USA.","DOI":"10.1109\/IEMBS.2011.6090781"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"103315","DOI":"10.1016\/j.apergo.2020.103315","article-title":"Exploring the utility of EDA and skin temperature as individual physiological correlates of motion sickness","volume":"92","author":"Smyth","year":"2021","journal-title":"Appl. Ergon."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.ijpsycho.2022.03.006","article-title":"Detecting and predicting visually induced motion sickness with physiological measures in combination with machine learning techniques","volume":"176","author":"Keshavarz","year":"2022","journal-title":"Int. J. Psychophysiol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1313","DOI":"10.1080\/00140139.2022.2028902","article-title":"Mitigating motion sickness in automated vehicles with vibration cue system","volume":"65","author":"Li","year":"2022","journal-title":"Ergonomics"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1007\/s10055-024-01067-z","article-title":"Physiology-driven cybersickness detection in virtual reality: A machine learning and explainable AI approach","volume":"28","author":"Sameri","year":"2024","journal-title":"Virtual Real."},{"key":"ref_13","unstructured":"(1997). Mechanical Vibration and Shock\u2014Evaluation of Human Exposure to Whole-Body Vibration\u2014Part 1: General Requirements (Standard No. ISO 2631-1:1997)."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1646","DOI":"10.1080\/001401399184730","article-title":"Motion sickness in public road transport: The effect of driver, route and vehicle","volume":"42","author":"Turner","year":"1999","journal-title":"Ergonomics"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Hwang, J.U., Bang, J.S., and Lee, S.W. (2022, January 9\u201312). Classification of motion sickness levels using multimodal biosignals in real driving conditions. Proceedings of the 2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Prague, Czech Republic.","DOI":"10.1109\/SMC53654.2022.9945559"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"5442","DOI":"10.1109\/TITS.2023.3240407","article-title":"Motion sickness prediction based on dry EEG in real driving environment","volume":"24","author":"Bang","year":"2023","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_17","first-page":"3","article-title":"Enhancing CNN-based motion sickness detection using EEG signals weighted by motion sickness dose value","volume":"26","author":"Song","year":"2025","journal-title":"IEEE Sens. J."},{"key":"ref_18","first-page":"130847","article-title":"Motion sickness prediction in intelligent electric vehicles using collaborative subjective-objective data fusion","volume":"267","author":"Fu","year":"2025","journal-title":"Expert Syst. Appl."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Kim, S.Y., and Kim, Y.S. (2026). A study on autonomous driving motion sickness from the perspective of multimodal human signals. Sensors, 26.","DOI":"10.3390\/s26051675"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"132385","DOI":"10.1016\/j.eswa.2026.132385","article-title":"A comprehensive investigation of auditory-induced emotions combining psychoacoustic metrics, facial emotion recognition, and explainable deep learning","volume":"322","author":"Alabi","year":"2026","journal-title":"Expert Syst. Appl."},{"key":"ref_21","unstructured":"(1995). Mechanical Vibration, Road Surface Profiles-Reporting of Measured Data (Standard No. ISO 8608)."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1016\/j.bspc.2011.03.004","article-title":"A novel method for detecting R-peaks in electrocardiogram (ECG) signal","volume":"7","author":"Manikandan","year":"2012","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_23","unstructured":"Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology (1996). Heart rate variability: Standards of measurement, physiological interpretation, and clinical use. Circulation, 93, 1043\u20131065."},{"key":"ref_24","first-page":"797","article-title":"cvxEDA: A convex optimization approach to electrodermal activity processing","volume":"63","author":"Greco","year":"2015","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1023\/A:1022627411411","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1016\/j.bspc.2018.12.007","article-title":"Machine learning assessment of visually induced motion sickness levels based on multiple biosignals","volume":"49","author":"Li","year":"2019","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Recenti, M., Ricciardi, C., Aubonnet, R., Picone, I., Jacob, D., Svansson, H.A.R., Agnarsd\u00f3ttir, S., Karlsson, G.H., Baeringsd\u00f3ttir, V., and Petersen, H. (2021). Toward predicting motion sickness using virtual reality and a moving platform: Assessing brain, muscles, and heart signals. Front. Bioeng. Biotechnol., 9.","DOI":"10.3389\/fbioe.2021.635661"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"3272","DOI":"10.1038\/s41598-023-29765-9","article-title":"On the potential of transauricular electrical stimulation to reduce visually induced motion sickness","volume":"13","author":"Molefi","year":"2023","journal-title":"Sci. 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