In-cabin monitoring detects early onset dementia
Researchers in the US are using open source in-cabin monitoring with AI in vehicles to assess drivers for their risk of dementia.
Nursing, engineering and neuropsychology researchers at Florida Atlantic University (FAU) are testing and evaluating an open source readily and rapidly available, unobtrusive in-cabin monitoring and AI sensing system they have developed.
Worryingly, 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 dementias are eventually unable to drive safely, yet many remain unaware of their cognitive decline.
This technology could provide the first step toward widespread, low-cost early warnings of cognitive change for this large number of older drivers in the U.S. and elsewhere.
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The study is systematically examining how the in-cabin monitoring system could detect anomalous driving behaviour that is indicative of cognitive impairment. Few studies have reported on the use of continuous, unobtrusive sensors and related monitoring devices for detecting subtle variability 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.”
The study uses a naturalistic longitudinal design to obtain continuous information on driving behaviour that is being 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 the cognitive tests are administered.
Researchers are gauging abnormal driving such as getting lost, ignoring traffic signals and signs, near-collision events, distraction and drowsiness, reaction time and braking patterns. They also are looking at travel patterns such as number of trips, miles driven, miles on the highway, miles during the night and daytime, and driving in severe weather.
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 kept simple and compact by minimizing complex wiring, limiting the size of the sensing units, and limiting the number of sensors in a vehicle to support the unobtrusiveness of in-vehicle sensors. Each in-vehicle sensor system is comprised of two distributed sensing units: one for telematics data and the other for video data.
Inertial measurement unit data is processed to determine hard braking, hard accelerations and hard turns and GPS data. It also includes a timestamp, latitude, longitude, altitude, course over ground and the number of communicating satellites.
The video unit has built-in artificial intelligence functions that analyze video in real-time. The driver-facing camera is mounted in the left corner of the windshield and is directed to the driver’s face to analyze behaviour and facial expressions. The forward-facing camera is mounted under the rearview mirror and is used to record events external to the vehicle.
Driver-facing indices include face detection, eye detection (open or closed), yawning, distraction, smoking and mobile phone use. Behaviour indices include traffic sign detection (running a red light), object detection (pedestrian, cyclists, curbs, barriers or nearby vehicles), lane crossing, near-collision and pedestrian detection.
“These travel-pattern-related driver behaviour indices are known to be indicative of the changes in older drivers’ cognition and physical functions since they tend to incorporate deliberate avoidance strategies to compensate for age-related deficits,” said Tappen. “Driver behaviour indices are evaluated for each driver and are summarized on a daily, weekly and monthly basis and are classified into four categories.”
A total of 460 study participants will be recruited from the Broward and Palm Beach counties in Southeast Florida and classified into three diagnostic groups: mild cognitive impairment, early dementia and unimpaired for a trial.
“The innovation of our research project lies in the unobtrusive, rapidly and readily available in-vehicle sensing and monitoring system built upon modern open-source hardware and software using existing techniques to develop and customize the components and configure them for this new purpose,” said Tappen.
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