Reception Description

• Great strides have been made to reduce alcohol-related traffic fatalities since the late 1970s. Cars now have improved safety features, and the likelihood of swift, certain, and severe punishment for driving under the influence has increased. The legal alcohol limit was lowered from 0.1 to 0.08, and in the 1980s the minimum drinking age rose from 18 to 21 in all states. The option to plead out of an alcohol offense has been eliminated, and a blood alcohol content of 0.08 is considered a per se indicator of guilt. Refusing a breathalyzer test is grounds for the suspension of driving privileges. These measures have led to a substantial reduction in alcohol-related fatalities since the 1980s.

• From the early 1980s to 2000, the number of alcohol-related traffic fatalities was cut in half. It remained steady for several years but then began increasing again for the first time in decades. Since 2019, fatalities have continued to rise, and in 2022 there were 13,524 deaths where at least one driver was impaired.
  
  
• A research team at James Madison University believed that stricter laws, better enforcement, and shifting public attitudes had already done as much as they could. They concluded a passive technological solution was needed. In response, a team at the College of Integrated Science and Technology developed a device that performs a horizontal gaze nystagmus test—a technique police officers use to indicate possible impairment.
  
  
• This device connects to a vehicle’s brake system and prevents the car from going into gear if the driver fails the test. When the driver starts the car, they lower the visor and position their face in front of a screen. A dot appears and moves from side to side, and a camera tracks the driver’s eye movements. An algorithm analyzes these movements for nystagmus; failure stops the car from shifting into gear.
  
  
• The device is fully operational. It has been tested on 55 individuals both before and after they were spun in a chair, which briefly induces nystagmus, and it successfully detected the condition. The system has also been wired to a vehicle to confirm its ability to prevent the car from going into gear when impairment is detected.

Advisor Team Bios

Professor MacDonald has over 25 years of experience developing simulation models as decision support tools for local, state, and federal agencies, as well as for numerous businesses ranging from Boeing to small medical groups. His expertise lies in creating these models to aid in decision-making processes across various domains.

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Reid assists the team with commercialization through his capacity as JMU Entrepreneur in Residence (EIR) funded by Virginia Innovation Partnership Corporation (VIPC). 

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Dr. Ahmad Salman is an Associate Professor in the Department of Integrated Science and Technology at JMU. His research focuses on cryptography for secure communications in lightweight devices, as well as techniques for conserving battery power in those devices. He explores topics such as cyber-physical systems, the Internet of Things (IoT), and post-quantum cryptography. One of his projects involves developing a node that collects data (such as temperature and pH levels) from streams and sends it to a drone for analysis, without disturbing the surrounding ecosystem. Additionally, he investigates security and privacy concerns related to tracking devices used in hospitals while ensuring compliance with privacy regulations like HIPAA.

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Chase Coleman graduated in the Spring of 2024 and majored in Computer Science while minoring in both Robotics and Mathematics. He currently works as a technology consultant at Capgemini Government Solutions, where he also interned during his time at JMU. He specializes in cloud system architecture with multiple professional certifications in AWS and Azure.  

Chase greatly contributed to the beginning years of the HGN project by developing the first machine learning model to analyze users' test results. To facilitate testing, Chase programmed and installed a prototype user interface into a vehicle using a Raspberry Pi. He also utilized his AWS expertise to architect a cloud-based backend that provided computational support for the lightweight HGN Tester prototype. The research Chase gathered during IRB-approved test trials is now aiding the team in developing a deep learning model to further improve the HGN Tester's accuracy. 

LinkedIn Profile

Patrick Dodds is a senior majoring in Computer Science, graduating in the Spring 2025. Last summer, he interned at Booz Allen Hamilton, where he contributed to advanced projects requiring expertise in computing. He holds CompTIA Security+ and AWS Cloud Practitioner certifications, reflecting his focus on cybersecurity and cloud computing.

Patrick’s experience includes working with AWS servers and EC2 instances to build scalable solutions. He has also developed TinyML applications using Raspberry Pi and Jetson Nano. On the Horizontal Gaze Nystagmus (HGN) project he is working on integrating the software into the hardware in a jetson nano to be able to run the test and have a facial recognition integrated as well. Additionally, Patrick has skills in embedded systems, machine learning, programming LED lighting systems, blending software, and hardware integration.

LinkedIn Profile

Abdullah Alghoniemy is a second-year Master’s student in Computer Science at JMU. Over the past year, he interned as a Software Engineer at Ellucian, an EdTech company in Reston, VA. He also contributed to a research project at JMU, developing a machine learning-based solution to forecast real-time parking availability and recommend parking options to users before reaching their destination.  On the horizontal gaze nystagmus project, Abudllah is leading the development of machine learning algorithms to improve the accuracy of detecting horizontal gaze nystagmus.

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