Objective: This study addresses the need to measure and monitor objective, real-world driver safety behavior in at-risk drivers with age-related dysfunction. Older are at risk for cognitive visual dysfunction, which may reduce mobility increase errors that lead crashes. Understanding patterns of behavior, exposure, cognitive–perceptual processes underlying environmental context older requires new approaches.Methods: We assessed exposure vehicle control related steering, braking, accelerating...

Our goal is to address the need for driver-state detection using wearable and in-vehicle sensor measurements of driver physiology health. To this goal, we deployed systems, sensors, procedures capable quantifying real-world driving behavior performance in at-risk drivers with insulin-dependent type 1 diabetes mellitus (DM). We applied these methodologies over 4 weeks continuous observation quantify differences realworld profiles associated physiologic changes DM (N=19) without (N=14)....

The current paper implements a methodology for automatically detecting vehicle maneuvers from telemetry data under naturalistic driving settings. Previous approaches have treated maneuver detection as classification problem, although both time series segmentation and are required since input continuous. Our objective is to develop an end-to-end pipeline the frame-by-frame annotation of studies videos into various events including stop lane-keeping events, lane changes, left-right turning...

Driver behavior analysis plays an important role in driver assistance systems. A driver's face and head pose hold the key towards understanding whether attention concentration are on road while driving. Naturalistic driving studies (NDS) allow observing drivers real-time under naturalistic traffic conditions. Yet, data collected NDS often comprise low-resolution videos usually with more challenging camera positions compared to controlled studies. For instance, when is not directly facing...

Objective: Our goal is to measure real-world effects of at-risk driver physiology on safety-critical tasks like driving by monitoring behavior and in real-time. Drivers with type 1 diabetes (T1D) have an elevated crash risk that linked abnormal blood glucose, particularly hypoglycemia. We tested the hypotheses (1) T1D drivers would overall impaired vehicle control relative without diabetes, (2) At-risk patterns be at-risk, in-vehicle physiology, (3) show more recent hypoglycemia prior...

In this paper, we present a novel model to detect lane regions and extract departure events (changes incursions) from challenging, lower-resolution videos recorded with mobile cameras. Our algorithm used Mask-RCNN based detection as pre-processor. Recently, deep learning-based models provide state-of-the-art technology for object combined segmentation. Among the several learning architectures, convolutional neural networks (CNNs) outperformed other machine models, especially region proposal...

Objectives We test the hypothesis that clinical measures of age‐related cognitive, visual, and mobility impairments negatively impact older driver speed limit compliance to advance method developments improve patient care screen, identify, advise at‐risk drivers. Design Real‐world behaviors adults who had a range abilities (measured with standardized, tests) were assessed in environmental context (e.g., limit, traffic density, roadway type). Older was measured constant zones at transition...

Driving is a complex, everyday task that impacts patient agency, safety, mobility, social connections, and quality of life. Digital tools can provide comprehensive real-world (RW) data on driver behavior in patients with Parkinson's disease (PD), providing critical status treatment efficacy the patient's own environment.This pilot study examined use driving as RW digital biomarker PD symptom severity dopaminergic therapy effectiveness.Naturalistic (3974 drives) were collected for 1 month...

Our goal is to improve driver safety predictions in at-risk medical or aging populations from naturalistic driving video data. To meet this goal, we developed a novel model capable of detecting and tracking unsafe lane departure events (e.g., changes incursions), which may occur more frequently populations. The detects tracks roadway markings challenging, low-resolution videos using semantic detection pre-processor (Mask R-CNN) utilizing the driver's forward region, demarking convex hull...

Objective To quantify vehicle control as a metric of automobile driving performance in patients with rheumatoid arthritis (RA). Methods Naturalistic assessments were completed active RA and controls without disease. Data collected using in‐car, sensor‐based instrumentation installed the participants’ own vehicles to observe typical habits. disease status, activity, functional status associated (lateral [steering] longitudinal [braking/accelerating] acceleration variability) mixed‐effect...

Naturalistic driving studies (NDS) are an increasingly popular method to research behavior. They often result in large amounts of data varying source and format (videos, spatial, time-series data). Traditional processing systems analytical methods not equipped handle the influx data, ranging from terabytes petabytes. Previously, big analytics platforms have been designed address specific use cases intelligent transport such as traffic flow prediction, transportation planning, safety....

Objective Parkinson's disease (PD) impairs motor and non-motor functions. Driver strategies to compensate for impairments, like avoiding driving in risky environments, may reduce on-road risk at the cost of decreasing driver mobility, independence, quality life (QoL). It is unclear how PD symptoms link exposure, strategies, QoL. We assessed associations between exposure (1) overall, (2) (3) relationship

Abstract Background Cognitive decline is a leading early predictor of dementia risk and may be evident in real‐world (RW) digital data even before patients ever present to clinic. This study examined the feasibility using video sensor profiles real world driving adverse weather as marker risk. Method 70 older drivers (mean age = 75.6 years) without participated RW for 2, 3‐month periods, one year apart (Wang, et al. 2021) . Self‐reported demographics neuropsychological tests relevant were...

Quantify circadian rest-activity rhythms in persons taking dopaminergic medications for Parkinson's Disease (PD), using real-world (RW) outputs from personal devices (actigraphy) as actionable clinical data PD management.

This pilot study tackles the overarching need for driver-state detection through real-world measurements of driver behavior and physiology in at-risk drivers with type 1 diabetes mellitus (DM). 35 (19 DM, 14 comparison) participated. Real-time glucose levels were measured over four weeks continuous monitor (CGM) wearable sensors. Contemporaneous driving performance in-vehicle video electronic sensor instrumentation packages. Results showed clear links between (particularly hypoglycemia)...

The use of naturalistic driving studies (NDSs) for driver behavior research has skyrocketed over the past two decades. Intersections are a key target traffic safety, with up to 25-percent fatalities and 50-percent injuries from crashes in United States occurring at intersections. NDSs increasingly being used assess intersections devise strategies improve intersection safety. A common challenge NDS is need combine spatial locations driver-visited concurrent video clips trajectories extract...

<h3>Objective:</h3> Our objective is to detect age-related cognitive decline from driver behavior. The overarching goal develop real-world, digital biomarkers of early dementia, including Alzheimer's disease (AD), inform clinical care and intervention. <h3>Background:</h3> Cognitive affects driving abilities. Driver behavior patterns, in turn, index Roadway environments present varying challenges, revealing strategies accommodating challenges. Strategies map decline, even transitional...