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arXiv:2310.02351 (stat)
[Submitted on 3 Oct 2023 ]

Title: Investigating Speed Deviation Patterns During Glucose Episodes: A Quantile Regression Approach

Title: 研究血糖事件期间速度偏差模式:分位数回归方法

Authors:Aparna Joshi, Jennifer Merickel, Cyrus V. Desouza, Matthew Rizzo, Pujitha Gunaratne, Anuj Sharma
Abstract: Given the growing prevalence of diabetes, there has been significant interest in determining how diabetes affects instrumental daily functions, like driving. Complication of glucose control in diabetes includes hypoglycemic and hyperglycemic episodes, which may impair cognitive and psychomotor functions needed for safe driving. The goal of this paper was to determine patterns of diabetes speed behavior during acute glucose to drivers with diabetes who were euglycemic or control drivers without diabetes in a naturalistic driving environment. By employing distribution-based analytic methods which capture distribution patterns, our study advances prior literature that has focused on conventional approach of average speed to explore speed deviation patterns.
Abstract: 鉴于糖尿病的日益普遍,人们越来越关注糖尿病如何影响日常功能,如驾驶。 糖尿病的血糖控制并发症包括低血糖和高血糖发作,这可能损害安全驾驶所需的认知和心理运动功能。 本文的目的是在自然驾驶环境中确定糖尿病患者在急性血糖变化期间的驾驶速度行为模式,这些患者处于正常血糖状态或没有糖尿病的对照驾驶员。 通过采用捕捉分布模式的基于分布的分析方法,我们的研究推进了之前文献的研究,这些文献主要关注平均速度的传统方法来探索速度偏差模式。
Comments: 6 pages, 2 figures, 5 Tables, Accepted and Presented at IEEE ITSC 2023 Conference in Bilbao Spain
Subjects: Applications (stat.AP) ; Machine Learning (cs.LG)
Cite as: arXiv:2310.02351 [stat.AP]
  (or arXiv:2310.02351v1 [stat.AP] for this version)
  https://doi.org/10.48550/arXiv.2310.02351
arXiv-issued DOI via DataCite

Submission history

From: Aparna Joshi [view email]
[v1] Tue, 3 Oct 2023 18:27:34 UTC (438 KB)
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