Unfolding the dynamics of driving behavior: a machine learning analysis from Germany and Belgium

The presentation contains the result of a rheoencephalographic study of 67 sons and daughters of the patients with ischemic stroke of a hemispheric localization. Some rheoencephalographic signs testifying to the presence of a cerebro-vascular deficiency in the relatives of the probands were depicted. The use of nitroglycerin sample permitted to reveal in practically healthy relatives certain REG changes which exceeded the normal limits. These shifts were mainly observed in the elder age group of the investigated individuals (above 40 years). The obtained data can promote detection of those ind... Mehr ...

Verfasser: Roussou, Stella
Michelaraki, Eva
Katrakazas, Christos
Afghari, Amir Pooyan
Al Haddad, Christelle
Alam, Md Rakibul
Antoniou, Constantinos
Papadimitriou, Eleonora
Brijs, Tom
Yannis, George
Dokumenttyp: Artikel
Erscheinungsdatum: 2024
Verlag/Hrsg.: SPRINGERONE NEW YORK PLAZA
Schlagwörter: On-road field trials / Driving behavior / Long-short-term-memory network (LSTM) / Neural network / Machine learning
Sprache: Englisch
Permalink: https://search.fid-benelux.de/Record/base-28959798
Datenquelle: BASE; Originalkatalog
Powered By: BASE
Link(s) : http://hdl.handle.net/1942/43389

The presentation contains the result of a rheoencephalographic study of 67 sons and daughters of the patients with ischemic stroke of a hemispheric localization. Some rheoencephalographic signs testifying to the presence of a cerebro-vascular deficiency in the relatives of the probands were depicted. The use of nitroglycerin sample permitted to reveal in practically healthy relatives certain REG changes which exceeded the normal limits. These shifts were mainly observed in the elder age group of the investigated individuals (above 40 years). The obtained data can promote detection of those individuals who are predisposed to atherosclerosis and hypertensive disease, and to conduct medico-prophylactic measures in due time. ; The research was funded by the European Union’s Horizon 2020 i-DREAMS project (Project Number: 814761) funded by European Commission under the MG-2-1-2018 Research and Innovation Action (RIA).