Could Our Vehicles be First Responders in the Near Future?

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By Ken Dropkiewski (ken@prime-core.com)

While no hard statistics exist as to the exact number of motor vehicle accidents and concurrent heart attacks, the article 5 Facts About the Trend of Heart Attacks While Driving, makes a compelling case for why more people are suffering acute MIs while driving.

– An aging population that is still on the road

– Longer commutes

– Increased incidence of heart disease

– Increased incidence of AMI

The final reason —drum roll, please— you guessed it! Traffic. In 2009, one study found that sitting in traffic for one hour or being in heavy traffic for the same amount of time, nearly triples your risk of heart attack.

Safety of other passengers and those in other vehicles, as well as bystanders, are all at risk when the driver suffers a debilitating health issue. So, how can we decrease the incidence of coronary events while driving, when we cannot reasonably predict when an event will occur and to whom? The answer may be coming soon but from an unexpected source.

Let’s Go Places

The car manufacturer, Toyota, has been working to develop in-car technology that can predict, recognize, and respond to a myriad of health emergencies including heart attacks. After receiving a grant, engineers studied what type of computational systems, along with certain physiological monitors, could be integrated into vehicles to meet their safety objectives.

“The study took about seven months, and we identified the challenges, potential solutions, hardware options and algorithmic approaches that could be potentially used. But we concluded that cardiac events were conditions that are more feasible to detect with technology in the vehicle.” Pujitha Gunaratne, Ph.D., principal scientist for the Toyota Collaborative Safety Research Center

Moving Forward

Research is ongoing as various algorithms are being applied to health technology with the goal of coming up with a system that will predict the occurrence of a heart attack in real-time. Machine learning models are being used to develop the technology that many are hopeful will hit the road by 2020.

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