Background: Head-on-head impacts are a risk factor for concussion, which is a concern for sports. Computer vision frameworks may provide an automated process to identify head-on-head impacts, although this has not been applied or evaluated in rugby.
Methods: This study developed and evaluated a novel computer vision framework to automatically classify head-on-head and non-head-on-head impacts.
Several microtechnology devices quantify the external load of team sports using Global Positioning Systems sampling at 5, 10, or 15 Hz. However, for short, explosive actions, such as collisions, these sample rates may be limiting. It is known that very high-frequency sampling is capable of capturing changes in actions over a short period of time.
View Article and Find Full Text PDFWomen represent a substantial portion of the US workforce. However, injury and fatality rates for female workers have, historically, remained lower than rates for male workers. Fatal occupational data from the Census of Fatal Occupational Injuries (CFOI) and nonfatal injury data from the National Electronic Injury Surveillance System-Occupational Supplement (NEISS-Work) for the years 1998-2022 were examined to produce rate ratios of male to female fatal and nonfatal occupational injury rates for all workers in the United States.
View Article and Find Full Text PDFBackground: Child health equity is influenced by complex systemic factors, including structural racism, socioeconomic disparities, and access to resources. Traditional public health interventions often target individual behaviors, but there is a growing need for systems approaches that address these root causes. This study examines coalition members' perspectives on promoting child health equity in Milwaukee as a result of participating in an intervention that includes Community-based System Dynamics (CBSD).
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