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

汽车为医疗保健监控提供了巨大的机会。首先，大多数美国人每天都开车，病人花在车里的时间错过了监测他们身体状况和整体健康状况的机会。该项目的目标是开发和评估自动车载监测技术，以监测医疗状况的早期症状和患者的药物中断，并提供预防性护理。具体来说，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/多动障碍（ADHD），这是一种普遍的慢性疾病，如果不加以控制，可能会对健康和生活质量造成已知的负面影响。利用驾驶行为监测ADHD症状的方法可以应用于许多其他医疗状况（如糖尿病、视力减退、中毒、疲劳或心脏病发作），从而将医疗管理转变为实时感知和管理。从驾驶行为中识别所有这些情况，并向适当的代理人发出警报，可能会改变我们对健康监测的看法，从而挽救生命，减少伤害。该项目的主要目标是利用在驾驶过程中通过机器学习收集的大量健康数据，以检测由于失控的ADHD症状而导致的行为细微变化，例如，在注意力不集中发作之前指示它们的发作。研究小组将通过实验室驾驶模拟器和道路研究，调查不同药物使用状态下ADHD患者的个体化行为和车辆控制行为模式。该团队将开发一个基于案例和情境推理的机器学习框架，将患者当前的驾驶行为与之前记录的与不同ADHD症状相对应的驾驶行为相匹配。关键的机器学习挑战是定义适当的相似性度量来比较驾驶行为，考虑到我们研究中确定的ADHD驾驶行为的关键特征。该团队将通过驾驶模拟器实验以及使用载着真实患者的仪表汽车来评估所提出的方法识别和区分不同的失控多动症症状的准确性，这些症状对多动症患者的长期治疗有影响。

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.