Browsing by Author "Bland, Megan L."

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    Bicycle Helmet STAR Protocol
    Bland, Megan L.; McNally, Craig; Rowson, Steven (Virginia Tech, 2018-06-25)
    This document details the protocol used to rate adult bicycle helmets based on concussion risk according to the Virginia Tech Helmet Ratings.
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    Development of the STAR Evaluation System for Assessing Bicycle Helmet Protective Performance
    Bland, Megan L.; McNally, Craig; Zuby, David S.; Mueller, Becky C.; Rowson, Steven (Biomedical Engineering Society, 2019-08-01)
    Cycling is a leading cause of mild traumatic brain injury in the US. While bicycle helmets help protect cyclists who crash, limited biomechanical data exist differentiating helmet protective capabilities. This paper describes the development of a bicycle helmet evaluation scheme based in real-world cyclist accidents and brain injury mechanisms. Thirty helmet models were subjected to oblique impacts at six helmet locations and two impact velocities. The summation of tests for the analysis of risk (STAR) equation, which condenses helmet performance from a range of tests into a single value, was used to summarize measured linear and rotational head kinematics in the context of concussion risk. STAR values varied between helmets (10.9–25.3), with lower values representing superior protection. Road helmets produced lower STAR values than urban helmets. Helmets with slip planes produced lower STAR values than helmets without. This bicycle helmet evaluation protocol can educate consumers on the relative impact performance of various helmets and stimulate safer helmet design.
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    Ranges of Injury Risk Associated with Impact from Unmanned Aircraft Systems
    Campolettano, Eamon T.; Bland, Megan L.; Gellner, Ryan A.; Sproule, David W.; Rowson, Bethany M.; Tyson, Abigail M.; Duma, Stefan M.; Rowson, Steven (2017-12)
    Regulations have allowed for increased unmanned aircraft systems (UAS) operations over the last decade, yet operations over people are still not permitted. The objective of this study was to estimate the range of injury risks to humans due to UAS impact. Three commercially-available UAS models that varied in mass (1.2-11 kg) were evaluated to estimate the range of risk associated with UAS-human interaction. Live flight and falling impact tests were conducted using an instrumented Hybrid III test dummy. On average, live flight tests were observed to be less severe than falling impact tests. The maximum risk of AIS 3+ injury associated with live flight tests was 11.6%, while several falling impact tests estimated risks exceeding 50%. Risk of injury was observed to increase with increasing UAS mass, and the larger models tested are not safe for operations over people in their current form. However, there is likely a subset of smaller UAS models that are safe to operate over people. Further, designs which redirect the UAS away from the head or deform upon impact transfer less energy and generate lower risk. These data represent a necessary impact testing foundation for future UAS regulations on operations over people.