Creating Biomedical Technologies to Improve Health

2017 BESIP Project

Health Behavior Branch
NICHD
Mentor Name: 
Bruce Simons-Morton, Ed.D., M.P.H.
Johnathon Ehsani, Ph.D.
Mentor Email: 
Mentor Telephone: 
(301) 496-5674
Computational Bioscience and Engineering Laboratory, Office of Intramural Research
CIT
Mentor Name: 
Thomas Pohida Project #3
Raisa Freidlin, D.Sc.
Mentor Telephone: 
(301) 435-2904

Laboratory and Project Description

mHealth Smartphone Application to Measure Risky Driving Behavior and Predict Crashes
 
Motor vehicle crashes are a leading cause of injury and death among adolescents. Teens 16 to 19 years old are four times more likely to be involved in a car accident than other age categories; with over 5000 fatal and about 4000 serious injuries per year. The Simons-Morton group has a long-standing interest in developing new approaches to understanding driving risk, such as the use of objective real-time in-vehicle measurements. A recent study indicates that elevated gravitational forces (g-forces) are useful when assessing driving risk, and predictive of crashes and near crashes among teenage drivers. G-force and other measures have the potential to transform the way driving risk is estimated, and open the door to population based injury prevention.
 
Approximately three out of four teenagers in the U.S. own a smartphone. With integrated sensors, wireless communication, and computing power, smartphones could be leveraged for driving research, and perhaps also as a direct means to reduce driving risk by providing timely feedback to teens and parents. For this project, g-force events and mileage driven will be captured using phone accelerometers and global positioning system (GPS) technology. The overall goal is to develop simple, non-proprietary smartphone-based tools (i.e., mHealth) to facilitate and advance driving research. By reducing the costs and other challenges associated with the collection of objective driving related data, mHealth solutions and methods could enable both increased and innovative driving research, and extend the reach to previously unmeasured populations, for example, in lower socio-economic strata and low-income countries.
 
A g-force Android smartphone application has been developed and validated with on-road testing utilizing vehicles with diverse instrumentation. While Android is the globally dominant smartphone operating system, the majority of U.S. teens own an iPhone. As U.S. teenage drivers are the initial target population for this driving research, an iPhone iOS version of an enhanced application is needed. Existing and new application functionality includes: interfacing with phone hardware sensors, signal processing of raw sensor signals, data parsing and storing locally and to servers, automating data collection (e.g., distinguishing walking, public transit, passenger), and algorithm-based predicting of crashes. In-house laboratory testing of the iPhone g-force application measurement reproducibility and sensitivity will be followed with in-vehicle validation on a test track.
 
A BESIP student working on this project should have a specific interest in iPhone application development, signal processing, and instrumentation. Team discussions will also include data analysis and centralization, emerging mHealth paradigms, and epidemiology research. Working closely with the interdisciplinary team, the intern will help develop and test the iPhone g-force application, and contribute to top-level design and concepts associated with the long term goal of providing a first-of-a-kind open-source validated mHealth tool facilitating driving research.
 
Simons-Morton lab: Conducts observational and experimental studies to improve our understanding of the nature of teen driving risk. How teens learn to drive, how driving performance changes under different conditions (e.g. with teen passengers, at night), and individual characteristics that predict crash risk, are the research questions that are of primary interest. Naturalistic driving studies, driving simulator experiments and surveys are the methods we use to answer these research questions.
 
Pohida lab: Provides electrical, electronic, electro-optical, mechanical, computer, and software engineering expertise to NIH projects that require in-house technology development. Collaborations involve advanced signal transduction and data acquisition; real-time signal and image processing; control and monitoring systems (e.g., robotics and process automation); and rapid prototype development. Collaborations result in the design of first-of-a-kind biomedical/clinical research systems, instrumentation, and methodologies.