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New Technology Uses In-Cabin Monitoring to Detect Early Onset Dementia in Drivers

TL;DR

    • FAU researchers pioneer a new approach to detect early onset dementia in drivers using in-cabin monitoring and AI technology.

    • The study utilizes a longitudinal design to analyze driving behavior over three years, aiming to identify subtle indicators of cognitive decline.

    • Advanced sensor technology enables unobtrusive monitoring, offering a potential solution for early detection of dementia in older drivers.

Researchers at Florida Atlantic University (FAU) are conducting a groundbreaking study that employs open-source in-cabin monitoring and AI sensing systems in vehicles to assess drivers for their risk of dementia. This innovative approach aims to provide early warnings of cognitive changes, a crucial step toward enhancing road safety.

The need for such technology is underscored by concerning statistics: an estimated 4 to 8 million older adults with mild cognitive impairment are currently driving in the United States, and one-third of them will develop dementia within five years. Individuals with progressive dementia are eventually unable to drive safely, yet many remain unaware of their cognitive decline.

The study systematically examines how the in-cabin monitoring system can detect anomalous driving behavior indicative of cognitive impairment. Few studies have reported on the use of continuous, unobtrusive sensors and related monitoring devices for detecting subtle variabilities in the performance of highly complex everyday activities over time.

“The neuropathologies of Alzheimer’s disease have been found in the brains of older drivers killed in motor vehicle accidents who did not even know they had the disease and had no apparent signs of it,” said Prof. Ruth Tappen, the principal investigator. “The purpose of our study arose from the importance of identifying cognitive dysfunction as early and efficiently as possible. Sensor systems installed in older drivers’ vehicles may detect these changes and could generate early warnings of possible changes in cognition.”

Longitudinal study design

The study employs a naturalistic longitudinal design to obtain continuous information on driving behavior, which is compared with the results of extensive cognitive testing conducted every three months for three years. A driver-facing camera, forward-facing camera, and telematics unit are installed in the vehicle, and data is downloaded every three months when cognitive tests are administered.

Researchers are gauging abnormal driving behaviors such as getting lost, ignoring traffic signals and signs, near-collision events, distraction and drowsiness, reaction time, and braking patterns. They are also analyzing travel patterns, including the number of trips, miles driven, highway miles, night and daytime driving, and driving in severe weather.

Open-Source Hardware and Software Approach

The in-vehicle sensor network developed by FAU researchers in the College of Engineering and Computer Science uses open-source hardware and software components to reduce the time, risks, and costs associated with developing in-vehicle sensing units. The in-cabin sensor systems are designed to be simple and compact, minimizing complex wiring, sensor size, and sensor quantity to ensure unobtrusiveness.

Each in-vehicle sensor system comprises two distributed sensing units: one for telematics data and the other for video data. The inertial measurement unit processes data to determine hard braking, accelerations, turns, and GPS data. The video unit has built-in AI functions that analyze video in real-time, including driver-facing indices like face detection, eye detection, yawning, distraction, smoking, and mobile phone use, as well as behavior indices like traffic sign 

detection, object detection, lane crossing, near-collision, and pedestrian detection.

Disclaimer. The information provided is not trading advice. Cryptopolitan.com holds no liability for any investments made based on the information provided on this page. We strongly recommend independent research and/or consultation with a qualified professional before making any investment decisions.

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John Palmer

John Palmer is an enthusiastic crypto writer with an interest in Bitcoin, Blockchain, and technical analysis. With a focus on daily market analysis, his research helps traders and investors alike. His particular interest in digital wallets and blockchain aids his audience.

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