SCH: INT: Collaborative Research: Diagnostic Driving: Real Time Driver Condition Detection Through Analysis of Driving Behavior

SCH：INT：协作研究：诊断驾驶：通过驾驶行为分析实时检测驾驶员状况

• 批准号: 1521972
• 负责人: Avelino Gonzalez
• 金额: $ 31.4万
• 依托单位: The University of Central Florida Board of Trustees
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1521972&HistoricalAwards=false
• 关键词: SCH; INT; Collaborative; Research; Diagnostic

The automobile presents a great opportunity for healthcare monitoring. For one, most Americans engage in daily driving, and patient's time spent in vehicles is a missed opportunity to monitor their condition and general wellbeing. The goal of this project is to develop and evaluate technology for automatic in-vehicle monitoring of early symptoms of medical conditions and disrupted medications of patients, and to provide preventive care. Specifically, in this project we will focus on Attention-Deficit/Hyperactivity disorder (ADHD) in teenagers and young adults, a prevalent chronic medical condition which when uncontrolled has the potential for known negative health and quality of life consequences. The approach of using driving behavior to monitor ADHD symptoms could be applied to many other medical conditions (such as diabetes, failing eyesight, intoxication, fatigue or heart attacks) thereby transforming medical management into real-time sensing and management. Identification of all these conditions from driving behavior and alerting the proper agent could transform how we think about health monitoring and result in saved lives and reduced injuries.The main goal of this project is to leverage the large amounts of health data that can be collected while driving via machine learning, in order to detect subtle changes in behavior due to out-of-control ADHD symptoms that can, for example, indicate the onset of episodes of inattention before they happen. Via lab-based driving simulator as well as on-road studies, the research team will investigate the individualized behaviors and patterns in vehicle control behaviors that are characteristic of ADHD patients under various states of medication usage. The team will develop a machine learning framework based on case-based and context-based reasoning to match the current driving behavior of the patient with previously recorded driving behavior corresponding to different ADHD symptoms. The key machine learning challenge is to define appropriate similarity measures to compare driving behavior that take into account the key distinctive features of ADHD driving behavior identified during our study. The team will evaluate the accuracy with which the proposed approach can identify and distinguish between different out-of-control ADHD symptoms, which are the implications for long-term handling of ADHD patients, via driving simulator experiments as well as using instrumented cars with real patients.

汽车为医疗保健监测提供了一个很好的机会。一方面，大多数美国人都会从事日常驾驶，而患者在车辆上花费的时间是一个失踪的机会来监控自己的状况和一般福祉。该项目的目的是开发和评估对医疗状况早期症状和患者药物破坏的早期症状自动监测技术的技术，并提供预防保健。具体而言，在这个项目中，我们将重点关注青少年和年轻人的注意力缺陷/多动症（ADHD），这是一种普遍存在的慢性病状况，当不受控制的情况下，这种病情可能会带来已知的负面健康和生活质量后果。使用驾驶行为来监测多动症症状的方法可以应用于许多其他医疗状况（例如糖尿病，视力失败，中毒，疲劳或心脏病发作），从而将医疗管理转化为实时感应和管理。通过驾驶行为和提醒适当的代理来识别所有这些条件，可以改变我们对健康监测的看法，并导致挽救生命和减少伤害。该项目的主要目标是利用可以通过机器学习驾驶时可以收集的大量健康数据，以便检测到因对控制的ADHD症状而导致的细微变化，例如，这些症状可能会发生，例如，这些症状会发生在这些过程中。通过基于实验室的驾驶模拟器以及公路研究，研究团队将研究在各种用药状态下，在多动症患者的特征中，车辆控制行为的个性化行为和模式。该团队将基于基于病例和基于上下文的推理开发机器学习框架，以与患者的当前驾驶行为相匹配，与先前记录的驾驶行为相对应与不同的ADHD症状相对应。关键的机器学习挑战是定义适当的相似性度量，以比较驾驶行为，这些驾驶行为考虑到我们研究期间确定的ADHD驾驶行为的关键特征。该团队将通过驱动模拟器实验以及与真实患者使用仪器的汽车，评估所提出的方法可以识别和区分不同控制ADHD症状的准确性和区分不同的对照ADHD症状。