Keeping your eyes on the road may get some assistance

Eye-tracking technology Researchers at the University of Missouri are looking at the importance of keeping your eyes on the road with two new uses of eye-tracking technology in relation to vehicle collision avoidance warnings and rear-end accidents.

MU scientists take a new look at the importance of keeping your eyes on the road

With the recent advances in vehicle-assisted safety technology and in-car displays, this old adage has a new meaning, thanks to two new applications of eye-tracking technology developed by researchers at the University of Missouri.

Designing a better collision avoidance warning

Observing how someone’s eyes change — specifically the pupil — while they respond to an alert given by a vehicle collision avoidance warning could one day help scientists design safer systems.

“Prior to a crash, drivers can be easily distracted by an alert from a collision avoidance warning — a popular feature in new vehicles — and we feel this could be a growing problem in distraction-related vehicle crashes,” said Jung Hyup Kim, an assistant professor of industrial and manufacturing systems engineering in the MU College of Engineering. “Therefore, a two-way communication channel needs to exist between a driver and a vehicle. For instance, if a driver is aware of a possible crash, then the vehicle does not have to warn the driver as much. However, if a vehicle provides an alert that, by itself, creates a distraction, it could also cause a crash.”

Kim and Xiaonan Yang, a graduate student at MU, watched how people’s pupils changed in response to their physical reactions to a collision avoidance warning by a vehicle-assisted safety system. Researchers believe they have enough data to begin the next step of developing a two-way communication model.

Evaluating rear-end accidents from a driver’s perspective

A person’s pupil could also help scientists find a way to decrease distracted driving crashes through a first-hand perspective into a driver’s behavior, according to Kim and Rui Tang, a graduate student at MU. Using a driving simulator at the MU College of Engineering, the researchers evaluated a driver’s physical behavior in real-time by focusing on the driver’s eyes as the crash happened.

“We saw the size of a person’s pupil changed depending on the behavioral response to the severity of the accident,” Kim said. “Now, we want to take that data, find common patterns and build a model to test how we could help decrease distracted-driving crashes.”

The conference papers, “Evaluating rear-end vehicle accident using pupillary analysis in a driving similar environment,” and “Pupillary response and EMG predict upcoming responses to collision avoidance warning,” were presented at the 2019 International Conference on Applied Human Factors and Ergonomics in Washington, D.C.

Learn more: Seeing is believing: Eye-tracking technology could help make driving safer

 

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Could risk for psychotic disorders be detected by neurological brain markers

Findings from MU study could help identify people at-risk

Help may be on the way for people who might lose contact with reality through a psychotic disorder, such as schizophrenia.

People who may hear and see things that are not there could have symptoms of psychosis, better known as psychotic disorders. Now, researchers at the University of Missouri have found neurological markers in the human brain that can be used to detect people at-risk for developing psychotic disorders and to understand when this risk has been successfully treated.

“Psychotic disorders like schizophrenia are often lifelong and disabling for individuals,” said John Kerns, professor of psychology in the MU College of Arts and Science. “These disorders have major public health and societal costs greater than cancer. A major goal of our current research is to understand the nature of psychosis risk so we can prevent years of suffering.”

Researchers said psychotic disorders are associated with increased levels of dopamine — a chemical released by nerve cells — in a subregion of the brain called the striatum. This area is wired to process positive versus negative feedback for learning, often resulting in a person’s thoughts and actions based on what they’ve experienced in the past. Therefore, researchers suggest that psychotic disorders involve a faulty feedback in learning that then drives a person’s faulty beliefs and perceptions. However, measuring levels of dopamine in people is costly, invasive and not feasible in everyday clinical practice. In this new study, MU researchers used an MRI at MU’s Brain Imaging Center and found that people at risk for psychotic disorders exhibit evidence of dysfunction in the striatum.

“This dysfunction is most evident when performing tasks where people need to learn from positive and negative feedback,” Kerns said. “For instance, we have found that the risk for psychotic disorders involves increased activation in the striatum for positive feedback, and negative feedback involves decreased activation in the same subregion of the brain.”

Researchers believe this pattern of activation could explain symptoms of psychotic disorders. For example, activation resulting from increased positive feedback could make a person’s assumption seem truer than it really is, meanwhile activation from decreased negative feedback could make someone less likely to discard negative ideas. The team will conduct future research to examine how well an MRI can predict the risk of psychotic disorders and whether prevention treatments can ‘normalize’ MRI scans. They hope that their research will help prevent psychotic disorders, improve the lives of millions of people and greatly reduce public health costs.

Learn more: Neurological brain markers might detect risk for psychotic disorders

 

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Safer drinking water using the new liquid forensics process

Analyzing the data
A tattoo removal laser machine sends out a series of brief flashes of light each lasting about 10 nanoseconds. The flashes of light travel through a fiber optic cable wrapped on one end with paint-on liquid electrical tape. The cable’s end, submerged in the liquid, converts the laser light into sound. The sound is recorded by a microphone and the data is analyzed in real time by this computer.

Team develops new method of measuring the speed of sound in liquids

Ping! The popular 1990 film, The Hunt for Red October, helped introduce sonar technology on submarines to pop culture. Now, nearly 30 years later, a team of scientists at the University of Missouri is using this same sonar technology as inspiration to develop a rapid, inexpensive way to determine whether the drinking water is safe to consume. Based on their results, the scientists said they can determine changes in the physical properties of liquids.

“If the water isn’t drinkable, then our method will tell you that something is wrong with the water,” said Luis Polo-Parada, an associate professor of pharmacology and physiology in the MU School of Medicine and investigator at the MU Dalton Cardiovascular Research Center. “For instance, if a facility removes salt from sea water in order for water to be safe for drinking, our method can help alert the facility to potential changes such as an issue with the desalination process.”

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The instrument is designed to analyze the quality of liquids using the photoacoustic effect, or the generation of sound waves after light is absorbed in a material. Drops of sea water, dairy milk or ionic liquids, a class of molten salt, were used in the study. The MU scientists believe this might be the first use of this technology to analyze such small liquid samples.

“Let’s use cymbals as an analogy,” said Gary A. Baker, associate professor of chemistry in the MU College of Arts and Science. “Sunlight causes the cymbals to heat up and create a constant ringing sound. Here, on a much smaller scale, we create the same effect by sending flashes of laser light at our tiny homemade cymbal, which is the tape, and measure the speed of the sound that is generated.”

The team is working to refine its recording methods and equipment to provide commercial industries with an inexpensive way to monitor the quality of liquids, such as the percentage of alcohol in alcoholic beverages, the amount of inferior oil in fraudulent olive oils, the quality of honey and the amount of sugar or sugar substitutes in soft drinks. They plan to publish updated results later this year.

How it works: A tattoo removal laser machine sends out a series of brief flashes of light each lasting about 10 nanoseconds. The flashes of light travel through a fiber optic cable wrapped on one end with paint-on liquid electrical tape. The cable’s end, submerged in the liquid, converts the laser light into sound. The sound is recorded by a microphone and the data is analyzed in real time.

Learn more:Liquid forensics’ could lead to safer drinking water, MU study finds

 

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A ballistocardiogram is a non-contact way of measuring changes in the cardiovascular system

Bed sensor technology
Ten years ago, Skubic’s team developed hydraulic sensors that can be placed under a bed mattress to measure a person’s heart rate and respiration rate. The team used those sensors on this hanging bed to gather data for the mathematical model used in this study.

MU scientists advance a way to track changes in a person’s cardiovascular system

Every heart beat sends blood flowing throughout the human body. While an electrocardiogram uses a contact approach to measure the electrical activity of the heart, a ballistocardiogram is a non-contact way of measuring the mechanical effect of the blood flow through the cardiovascular system.

Giovanna Guidoboni, Marjorie Skubic and a team at the University of Missouri are currently working to develop a standardized model to interpret the results of a ballistocardiogram that could provide an additional approach for early detection of various cardiovascular diseases. Ten years ago, Skubic’s team developed hydraulic sensors that can be placed under a bed mattress to measure a person’s heart rate and respiration rate. They noticed the waveforms were changing over time as people aged, indicating there was additional information coming from those measurements that could be used for tracking health changes.

“Right now, only five percent of the information in the ballistocardiogram is used, but if we can standardize the results, we can provide a map for understanding the underlying causes behind the real physiological motion of our bodies,” Guidoboni said. “This could help in early detection and prevention of cardiovascular diseases such as heart disease.”

Guidoboni joined Skubic’s team and created a mathematical model that allows the team to understand the additional information from the ballistocardiogram and move one step closer to a standardized model.

“Even when we stand or lie still, our mass redistributes inside our body and generates a bodily motion that can be captured with a ballistocardiogram,” Guidoboni said. “By applying our mathematical model, we can see information that we haven’t previously known about an individual’s cardiovascular system, such as the elasticity of the arteries, the contractility of the ventricles in the heart, or the viscoelasticity of the blood vessels. We built a virtual cardiovascular system by mathematically modeling the blood flow in our bodies.”

Learn more: A ‘virtual’ view with a little bit of math

 

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Using artificial intelligence to design billions of possible new materials

At the University of Missouri, researchers in the College of Engineering are applying one of the first uses of deep learning — the technology computers use to intelligently perform tasks such as recognizing language and driving autonomous vehicles — to the field of materials science

A team of MU researchers are applying one of the first uses of artificial intelligence principles to the field of materials science

Discovering how atoms — such as a single layer of carbon atoms found in graphene, one of the world’s strongest materials — work to create a solid material is currently a major research topic in the field of materials science, or the design and discovery of new materials. At the University of Missouri, researchers in the College of Engineering are applying one of the first uses of deep learning — the technology computers use to intelligently perform tasks such as recognizing language and driving autonomous vehicles — to the field of materials science.

“You can train a computer to do what it would take many years for people to otherwise do,” said Yuan Dong, a research assistant professor of mechanical and aerospace engineering and lead researcher on the study. “This is a good starting point.”

Dong worked with Jian Lin, an assistant professor of mechanical and aerospace engineering, to determine if there was a way to predict the billions of possibilities of material structures created when certain carbon atoms in graphene are replaced with non-carbon atoms.

“If you put atoms in certain configurations, the material will behave differently,” Lin said. “Structures determine the properties. How can you predict these properties without doing experiments? That’s where computational principles come in.”

Lin and Dong partnered with Jianlin Cheng, a William and Nancy Thompson Professor of Electrical Engineering and Computer Science at MU, to input a few thousand known combinations of graphene structures and their properties into deep learning models. From there, it took about two days for the high-performance computer to learn and predict the properties of the billions of other possible structures of graphene without having to test each one separately.

Researchers envision future uses of this artificial intelligence assistive technology in designing many different graphene related or other two-dimensional materials. These materials could be applied to the construction of LED televisions, touch screens, smartphones, solar cells, missiles and explosive devices.

“Give an intelligent computer system any design, and it can predict the properties,” Cheng said. “This trend is emerging in the material science field. It’s a great example of applying artificial intelligence to change the standard process of material design in this field.”

Learn more: Teaching computers to intelligently design ‘billions’ of possible materials

 

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