Virtual reality driving and brain injury in the clinic

诊所中的虚拟现实驾驶和脑损伤

基本信息

批准号：
10017286
负责人：
MARIA Teresa SCHULTHEIS
金额：
$ 39.43万
依托单位：
DREXEL UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-09-13 至 2024-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10017286
关键词：
Accidents Address Algorithms Assessment tool Automobile Driving Behavior Brain Injuries Clinic Clinical Clinical Protocols Clinical assessments Communities Complex Consensus Data Data Reporting Databases Follow-Up Studies General Population Generations Gold Human Individual Intervention Laboratories Life Literature Measures Methodology Methods Modeling Modification Monitor Moods Motor Vehicles Online Systems Outcome Outcome Measure Output Participant Patient Self-Report Pattern Performance Persons Population Predictive Value Process Process Assessment Quality of life Records Reporting Risk Risk Behaviors Safety Sampling Standardization Statistical Data Interpretation System Techniques Technology Thinness Work driving behavior driving skills evidence base experience follow-up innovation novel predictive modeling programs simulation virtual reality

项目摘要

Despite the long-standing literature that has demonstrated changes in driving capacity following brain injury (BI) – little is known about the relationship of these differences and increased risk for driver error or the prediction of long term driving outcome after BI. However, it is well-established that the loss of the driving privilege negatively impacts functional re-integration, mood and quality of life – resulting from the reduced ability to participate in various life activities, work and educational experiences. The challenge to increasing our understanding of how to best assess and predict driving performance after BI is two-fold. First, there is a need for novel assessment methodologies that can provide objective, detailed and repeatable metrics of driving performance. The current clinical gold standard – the behind the wheel (BTW) driving assessment, is over-dependent on subjective observations, lacks standardization, assesses only basic driving skills (due to safety limitation) and generates gross measures of performance (i.e., Pass/Fail). Second, there is a lack of follow-up studies that examine actual return to driving behaviors among individuals with BI. While some evidence for greater risk of crash involvement (often dichotomized as Yes/No) has been reported, these studies have relied heavily on self-reported data and offer little to no data about driver behaviors and/or modifications, risk-involvement, crash causing-behaviors or driving patterns. The proposed study aims to address these limitations and employs an established virtual reality driving simulator (VRDS) that outputs novel driving performance metrics that are currently not available thru clinical methodology. The VRDS generates detailed metrics that can differentiate between clinical populations. Specifically, the study will integrate VRDS into an existing clinical driving assessment program and evaluate 100 individuals with BI across the process of returning to drive (e.g., from assessment to follow- up) and a sample of healthy controls. All participants will be assessed with both current clinical protocols and VRDS. This will be followed by a 24 month follow-up study including an innovative, 3-platform approach (in- car video-monitoring, web-based self-report and driving records) to quantifying returned to driving behaviors. The data collected will be used to apply both traditional (Regression Models) and novel (Machine-Leaning Models) analytical techniques to generate predictive models of relevant outcome variables (i.e., risk involvement, crash-relevant errors) that can be used to inform tailored driver interventions and retraining.

尽管长期文献表明脑损伤 (BI) 后驾驶能力会发生变化，但人们对这些差异与驾驶员失误风险增加之间的关系或 BI 后长期驾驶结果的预测知之甚少。 - 确定失去驾驶权会对功能重新整合、情绪和生活质量产生负面影响——这是由于参与各种生活活动、工作和教育经历的能力下降造成的。增强我们对如何最好地评估和预测 BI 后的驾驶表现的理解面临着双重挑战，首先，需要能够提供客观、详细和可重复的驾驶表现指标的新评估方法。 – 驾驶（BTW）驾驶评估过度依赖主观观察，缺乏标准化，仅评估基本驾驶技能（由于安全限制）并生成粗略的表现衡量标准（即通过/失败）。是缺乏后续研究调查了 BI 患者实际恢复驾驶行为的情况，虽然有一些证据表明发生车祸的风险更大（通常分为“是”/“否”），但这些研究很大程度上依赖于自我报告的数据，并且提供的信息很少。没有关于驾驶员行为和/或修改、风险参与、导致碰撞的行为或驾驶模式的数据。拟议的研究旨在解决这些问题，并采用现有的虚拟现实驾驶模拟器（VRDS），该模拟器可以输出目前无法通过临床方法获得的新颖驾驶性能指标。将 VRDS 整合到现有的临床驾驶评估计划中，并在恢复驾驶的过程中（例如，从评估到随访）对 100 名个体进行 BI 评估，并抽取健康对照样本。 VRDS 随后将进行为期 24 个月的跟踪研究，包括采用创新的 3 平台方法（车内视频监控、基于网络的自我报告和驾驶记录）来量化返回的驾驶行为数据。将用于应用传统（回归模型）和新颖（机器学习模型）分析技术来生成相关结果变量（即风险参与、碰撞相关错误）的预测模型，这些模型可用于为量身定制的驾驶员干预措施提供信息和再培训。