DOI: 10.5281/zenodo.6560247
title: Parameters for Statistical Evaluation of Sensing Time Efectiveness for Assessment of Fitness to Drive

description: This repository contains a table with parameters (including sensing time - ST parameter) and an R script for statistical analysis. This is supplementary material for the preprint available on arXiv and titled "Sensing Time Effectiveness for Fitness to Drive Evaluation in Neurological Patients" authored by Nadica Miljković and Jaka Sodnik . ST parameter was calculated in overall 56 patients during selected scenario with pedestrian collision in a driving simulator produced by Nervtech. Together with other parameters, ST parameters are stored in tableParameters.csv, while R programming code for statistical analysis of all parameters is placed in statisticalAnalysis.R. <strong>Dataset contents</strong> tableParameters.csv, table with parameters, csv (comma-separated values) format statisticalAnalysis.R, code in R programming language for statistical analysis <strong>Table with parameters has the following structure</strong> column - no which is ordinary number in consecutive order from 1 to 56 column - id presents an internal patient's id column - st presents ST parameter in ms column - fitness presents a categorical variable and can be either fit-, unfit-, or conditionally-fit-to-drive (cond fit) column - speed at the collision onset in km/h column - ttc presents time-to-collision in s column - manual_correction is categorical variable: 0 means that no manual correction was required for ST calculation, while 1 means that manual correction was required column - igd presents initial gaze distance in pixels Missing data are presented with NA (Not Available). NOTE: Python code for ST calculation and sample eye tracker video are available on GitHub repository https://github.com/NadicaSm/Sensing-Time-Calculation-from-the-Eye-Tracker-Videos under GNU GPL license and released on Zenodo with doi (https://doi.org/10.5281/zenodo.6560419). If you find these parameters and R code useful for your own research and teaching class, please cite the following references: Miljković, N., & Sodnik, J. (2022). Parameters for Statistical Evaluation of Sensing Time Effectiveness for Assessment of Fitness to Drive [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6560246 Miljković, N., & Sodnik, J. (2022, May). Sensing Time Effectiveness for Fitness to Drive Evaluation in Neurological Patients. Preprint in <em>arXiv</em> (pp. 1-23). https://doi.org/10.48550/arXiv.2205.08942 Motnikar, L., Stojmenova, K., Štaba, U. Č., Klun, T., Robida, K. R., & Sodnik, J. (2020). Exploring driving characteristics of fit-and unfit-to-drive neurological patients: A driving simulator study. <em>Traffic Injury Prevention</em>, 21(6), 359-364. https://doi.org/10.1080/15389588.2020.1764547 <strong>Acknowledgements</strong> J.S. kindly acknowledges University Rehabilitation Institute Soča employees and the Nervtech team. Authors gratefully appreciate the support from Nenad B. Popović, PhD from University of Belgrade – School of Electrical Engineering for his valuable assistance in design of illustrations and for provided feedback for the initial manuscript structure. Also, both Authors thank Nebojša Jovanović, MSc from University of Belgrade - School of Electrical Engineering for his kind contribution to earlier stages of the project, especially for his work on developing Python code to capture sensing time parameter.
description: FUNDING: N.M. acknowledges amiable support from the Grant No. 451-03-68/2022-14/200103 funded by the Ministry of Education, Science, and Technological Development, Republic of Serbia. This research was financially supported by Slovenian Research Agency within the research program ICT4QoL - Information and Communications Technologies for Quality of Life, grant number P2-0246, and the research project Neurophysiological and Cognitive Profiling of Driving Skills, grant number L2-8178.