Information about project titled 'Assessment of model-based image-matching for future reconstruction of unhelmeted sport head impact kinematics'
Assessment of model-based image-matching for future reconstruction of unhelmeted sport head impact kinematics
|Details about the project - category||Details about the project - value|
|Project manager:||Gregory J. Tierney|
|Supervisor(s):||Ciaran K. Simms|
|Coworker(s):||Hamed Joodaki, Tron Krosshaug, Jason L. Forman, Jeff R. Crandall|
Introduction: Player-to-player contact inherent in many unhelmeted sports means that head impacts are a frequent occurrence. Model-Based Image-Matching (MBIM) provides a technique for the assessment of three dimensional linear and rotational motion patterns from multiple camera views of a head impact event, but the accuracy is unknown for this application.
Aim: The goal of this study was to assess the accuracy of the MBIM method relative to reflective marker-based motion analysis data for estimating six degree of freedom head displacements and velocities in a staged pedestrian impact scenario at 40 km/h.
Method: MBIM was compared with marker-based motion analysis in a staged pedestrian impact scenario at 40 km/h.
Results: showed RMS error was under 20 mm for all linear head displacements and 0.01–0.04 rad for head rotations. For velocities, the MBIM method yielded RMS errors between 0.42 and 1.29 m/s for head linear velocities and 3.53–5.38 rad/s for angular velocities.
Implications: This method is thus beneficial as a tool to directly measure six degree of freedom head positional data from video of sporting head impacts, but velocity data is less reliable. MBIM data, combined in future with velocity/acceleration data from wearable sensors could be used to provide input conditions and evaluate the outputs of multibody and finite element head models for brain injury assessment of sporting head impacts.