102165 20230519145440.0 doi 10.3390/math9060634 sideral 124108 ART-2021-124108 eng Frahi, T. Empowering advanced driver-assistance systems from topological data analysis 2021 Access copy available to the general public Unrestricted We are interested in evaluating the state of drivers to determine whether they are attentive to the road or not by using motion sensor data collected from car driving experiments. That is, our goal is to design a predictive model that can estimate the state of drivers given the data collected from motion sensors. For that purpose, we leverage recent developments in topological data analysis (TDA) to analyze and transform the data coming from sensor time series and build a machine learning model based on the topological features extracted with the TDA. We provide some experiments showing that our model proves to be accurate in the identification of the state of the user, predicting whether they are relaxed or tense. info:eu-repo/semantics/openAccess by http://creativecommons.org/licenses/by/3.0/es/ 2.592 2021 MATHEMATICS 21 / 333 = 0.063 2021 Q1 T1 0.538 2021 Computer Science (miscellaneous) 2021 Q2 Engineering (miscellaneous) 2021 Q2 2.9 2021 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Chinesta, F. Falcó, A. Badias, A. Universidad de Zaragoza (orcid)0000-0001-7639-6767 Cueto, E. Universidad de Zaragoza (orcid)0000-0003-1017-4381 Choi, H.Y. Han, M. Duval, J.L. 5004 605 Universidad de Zaragoza Dpto. Ingeniería Mecánica Área Mec.Med.Cont. y Teor.Est. 9, 6 (2021), 634 [16 pp.] Mathematics (Basel) Mathematics 2227-7390 2998295 http://zaguan.unizar.es/record/102165/files/texto_completo.pdf Versión publicada 2740842 http://zaguan.unizar.es/record/102165/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:102165 articulos driver 2023-05-18-14:31:31 ARTICLE