Multi-modal cloud based system for detecting early signs of driver cognitive decline and/or high risk driving behaviors
基于多模式云的系统,用于检测驾驶员认知能力下降和/或高风险驾驶行为的早期迹象
基本信息
- 批准号:10258737
- 负责人:
- 金额:$ 41.64万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2021
- 资助国家:美国
- 起止时间:2021-08-15 至 2023-07-31
- 项目状态:已结题
- 来源:
- 关键词:AffectAgeAlzheimer&aposs DiseaseAlzheimer&aposs disease related dementiaAttentionAutomobile DrivingAutomobilesBehaviorBiological MarkersBrain scanCaregiversClinicalCognitionCognitiveCollaborationsComplexComputer Vision SystemsComputer softwareConsultationsDataData AnalyticsDatabasesDerivation procedureDiagnosisElderlyEnrollmentEnvironmentEvaluationEventExerciseFailureGoalsHabitsHandHeart RateImpaired cognitionIndividualIndustryInsuranceMeasurementMeasuresMemoryMethodsMichiganModalityMonitorMotor VehiclesNeuropsychologyPatientsPatternPerformancePhasePhysiologic MonitoringPhysiologicalPilot ProjectsPositron-Emission TomographyPreventive measureReaction TimeRegistriesResearchRiskRisk AssessmentSafetyScientistSignal TransductionSkinSoftware DesignStressSurfaceSystemTechniquesTechnologyTestingTimeUniversitiesVolunteer GroupWireless TechnologyYoung driveradaptive devicesagedbasecloud basedcomplex dataconditioningdata exchangedesigndriving behaviorflexibilityhazardhigh riskhuman subjectimpaired driving performanceimprovedindexinginsightminimal riskmotor vehicle injurymultidisciplinarymultimodalitynew technologyphysical scienceprogramsrisky drivingroutine screeningscreeningsensorsuccessvolunteerwearable sensor technology
项目摘要
Project Summary/Abstract
Physical Sciences Inc. (PSI), in collaboration with Michigan State University (MSU) and the University of
Michigan (U-M), proposes to develop a novel technology that will non-intrusively monitor driver cognitive
decline and/or high-risk driving behavior that is especially suited for seniors (aged >65). Contextual
physiological biomarkers along with computer vision based drive quality measurements will be obtained
through a cloud-based acquisition system. Real-time risk assessments that are measures of cognitive status
will be performed on the cloud to provide comprehensive analysis on drive quality metrics for seniors with
varying levels of cognitive impairment.
Assessment of a driver’s cognitive status requires the use of a complex monitoring platform that can be
seamlessly installed into any automobile without interfering or adding complexity to driving. Therefore, our goal
is to develop a cloud-based, multi-modal routine cognitive/drive assessment system that combines a Smart
Steering Sleeve (S3), which monitors physiological biomarkers, along with a Computer Vision (CV) system that
provides contextual insight for biomarker changes and monitors a driver's habits. The Cloud-based Computer
Vision Smart Steering Sleeve (C-CVS3) sensor system will be capable of monitoring biomarkers such as heart
rate, hand gripping force, and the presence of excessive sweat from the skin surface indicating stress levels
affecting potential drive quality degradation, which are also factors expected to be associated with cognitive
impairment during critical events. Dash cameras provide temporal context including reaction times to
unexpected hazards in addition to driver’s safety measures, such as staying within the lane, and maintaining
distance to surrounding vehicles. The transmitted data to the cloud from both modalities will be used to derive
cognitive/drive quality indices (QI) for driving risks that can be associated with increased stress levels, attention
deficiency and/or cognitive decline in individuals. This modality will result in an add-on adaptive platform in
automobiles that gauges cognitive status/decline providing value to the auto insurance industry, RMV, as well
as to the caregivers of the cognitively impaired patient apart from the individual himself to identify a quantifiable
parameter before giving up driving or to seek additional cognitive diagnosis.
The basic functions of this comprehensive drive monitoring system will be evaluated in Phase I on a
number of individuals in a driving simulator. The technology will be improved in Phase II and will be further
tested during real-world driving scenarios.
项目总结/摘要
物理科学公司(PSI),与密歇根州立大学(MSU)和密歇根大学合作,
密歇根大学(U-M),提出开发一种新的技术,将非侵入性地监测驾驶员的认知
特别适合老年人(年龄>65岁)的衰退和/或高风险驾驶行为。上下文
将获得生理生物标记沿着基于计算机视觉的驾驶质量测量
通过一个基于云的采集系统实时风险评估是认知状态的衡量标准
将在云上执行,为老年人提供全面的驱动器质量指标分析,
不同程度的认知障碍
驾驶员认知状态的评估需要使用复杂的监控平台,
无缝安装到任何汽车中,不会干扰驾驶或增加驾驶复杂性。因此,我们的目标
是开发一个基于云的,多模式的日常认知/驱动评估系统,结合智能
转向套筒(S3),用于监测生理生物标志物,沿着计算机视觉(CV)系统,
为生物标志物的变化提供背景信息,并监控驾驶员的习惯。基于云的计算机
Vision Smart Steering Sleeve(C-CVS 3)传感器系统将能够监测心脏等生物标志物
速率、手握力以及皮肤表面是否存在过多汗液,表明压力水平
影响潜在的驾驶质量下降,这也是预期与认知相关的因素。
关键事件期间的损伤。仪表盘摄像头提供时间背景,包括反应时间,
除了驾驶员的安全措施外,还有意外的危险,例如留在车道内,
与周围车辆的距离。从这两种模式传输到云端的数据将用于导出
驾驶风险的认知/驾驶质量指数(QI),这些风险可能与压力水平、注意力
缺乏和/或认知能力下降。这种模式将导致一个附加的自适应平台,
汽车,衡量认知状态/下降提供价值的汽车保险业,RMV,以及
对于认知障碍患者的照顾者,除了个人本身,以确定一个可量化的
在放弃驾驶或寻求额外的认知诊断之前,
该综合驱动监控系统的基本功能将在第一阶段进行评估,
驾驶模拟器中的人数。该技术将在第二阶段得到改进,
在真实驾驶场景中进行测试。
项目成果
期刊论文数量(1)
专著数量(0)
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专利数量(0)
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