UNMC_Acronym_Vert_sm_4c
University of Nebraska Medical Center

Current Research

 

  • Monitoring real-world driver behavior for classification and early prediciton of Alzheimer's disease
    PI: Matthew Rizzo MD 
    Investigators: Jeff Dawson, ScD, Shauna Hallmark, PhD, Jennifer Merickel, PhD, Dan Murman, MD, Soumik Sarkar, PhD, Anuj Sharma, PhD, Ying Zhang, PhD
    Funding: National Institutes of Health (Aging)
    This project tackles grand challenge of using a person’s own vehicle as a passive-detection system for flagging potential age- and/or disease-related aberrant driving that may signal early warning signs of functional decline and incipient Alzheimer’s disease (AD), even before standard clinical tests. The proposal builds on current successes using sensor technologies and a taxonomy of driver errors to comprehensively characterize patterns of real-world older driver behavior, exposure, and safety over extended time frames. These ongoing achievements support an extraordinary opportunity in this research project to use a patient’s own vehicle and devices as diagnostic tools to rigorously classify and track heretofore inaccessible fingerprints of driver behavior, sleep, and mobility––in the real world––to develop models capable of early detection and prediction of functional decline in older patients at risk for AD.
  • Safety and efficacy of cannabidiol treatment in patients with PTSD
    PI: Matthew Rizzo, MD 
    Investigators: Jennifer Merickel, PhD, Ying Zhang, PhD, Brigette Soltis-Vaughan, MSN, APRN-BC, NP
    Funding: Foundation
    The objective of this proposal is to determine the safety and efficacy of cannabidiol (CBD) for treatment of patients with Post-Traumatic Stress Disorder (PTSD). Clinical interest in applications of cannabis plant products for the treatment of various mental health disorders has risen significantly in the past decade. CBD has been suggested for the treatment of psychopathologies associated with serotonergic dysfunction including mood and anxiety disorders and PTSD. This study is comprehensively evaluating the efficacy of CBD in treating PTSD, including effects on clinical symptoms, neurobiology, inflammation and real-world patient outcomes through objective measurements of patient safety, mobility, quality of life and sleep.
  • Modeling Multidimensional Risk in Real-World Drivers with Diabetes
    PI: 
    Matthew Rizzo, MD  
    Investigators: Cyrus Desouza, MBBS, Andjela Drincic, MD, Jennifer Merickel, PhD, Soumik Sarkar, PhD, Anuj Sharma, PhD
    Funding: Toyota Collaborative Safety Research Center
    The goal of this novel project is to detect and predict on-road risk from wearable sensor measurements of driver physiology, health, and behavior.  To achieve this goal, we are studying individuals with diabetes (type 1 and 2) and linking dynamic patterns of driving behavior and risk to an individual driver's physiology (glucose levels and heart rate), cognition, and health (sleep dysfunction and obesity).  The results of this projects will advance the development of gold standards methods and supportive in-vehicle technology, like advanced driver-assistance systems (ADAS), for safety-critical driver-state detection and prediction.
  • Feasibility and utility of the car as a platform for monitoring behavior as an index of driver health and disease
    PI: Jennifer Merickel, PhD 
    Investigators: Matthew Rizzo, MD
    Funding: Toyota Collaborative Safety Research Center
    The overarching objective of this project is to assess the feasibility and utility of monitoring driver behavior to detect health and disease and to provide a high-level innovative technology framework that uses passive-sensors in available vehicle technology to detect driver health and disease. This project is motivated by extensive evidence linking driver behavior profiles to functional abilities and their dysfunction as a surrogate to driver health and disease. Evidence suggests feasibility and utility to use the vehicle as a diagnostic tool to screen and index driver health and disease––including indexing diagnosis, severity, trajectory, and impact––ultimately informing patient care, supporting development and implementation of personalized medicine programs, and providing a platform for developing health interventions.
  • Deep Insight: Deep Extraction of Driver State from Naturalistic Driving Dataset 
    PI: Anuj Sharma, PhD 
    Investigators: Yaw Adu-Gyamfi, PhD, Jennifer Merickel, PhD, Matthew Rizzo, MD, Soumik Sarkar, PhD, Senem Velipasalar, PhD
    Funding: Federal Highway Administration
    Computer vision algorithms have been improved significantly to provide accurate detection, tracking and recognition of objects in general. Available public datasets are crucial for extracting key features thanks to ever developing machine learning and deep learning algorithms in general. Since cameras are becoming vastly available to be employed on vehicles to extract useful information for both autonomous driving and intelligent driver assistance, we are targeting to develop intelligent driver state estimation algorithms that are based on state-of-the-art detection and recognition using computer vision. One of the main drawbacks for naturalistic driving data is having low-resolution and noisy video data that limits the overall accuracy when we test with the models trained on clear images. We are proposing (i) comprehensive AI platform for data management, modeling and enhanced annotations, (ii) video quality enhancement using deep models, iii) face detection at acute angles, and (iv) Recurring network-based driver state estimation.
  • Driving Performance and Safety in Rheumatoid Arthritis
    PI: 
    Ted Mikuls, MD  
    Investigators: Kaleb Michaud, PhD, Jennifer Merickel, PhD, Matthew Rizzo, MD
    Funding: Rheumatology Research Foundation
    Automobile driving is an instrumental activity of daily living and is linked to health, well-being, and quality of life in patients with Rheumatoid Arthritis (RA).  Despite its importance, there is little prior research investigating driving risk in patients with RA.  To address this knowledge gap, we are using state-of-the-art simulation and naturalistic driving assessment technologies to link clinical metrics of RA disease severity to real-world driver behavior and mobility.  Results from this study will fill critical gaps in RA patient care and develop "toolkits" to preserve mobility and quality of life for patients with RA.
  • Linking Changing Disease to Real-World Driver Performance in Parkinson's Disease
    PI: 
    Danish Bhatti, MD  
    Investigators: John Bertoni, MD, Jennifer Merickel, PhD, Matthew Rizzo, MD, Ergun Uc, MD
    Funding: UNMC Skate-a-thon for Parkinson's
    Parkinson's Disease (PD) is a relatively common neurodegenerative disorder affecting movement, vision, cognition, sleep, and mood.  These impairments, in turn, can affect the performance and behavior of a patient with PD in safety-critical tasks such as automobile driving.  This pilot research study addresses real-world driving and risk in drivers with and without PD using in-vehicle sensors and activity monitors.  The results can help us to better understand how driving safety in PD depends on a driver's disease, medication dosage, and functional abilities.  The findings will ultimately help improve clinical recommendations, safety, mobility, and quality of life in patients with PD.