Oslo Sports Trauma Research Center

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Information about project titled 'Identifying ACL risk profile clusters in from cutting biomechanics through machine learning'

Identifying ACL risk profile clusters in from cutting biomechanics through machine learning

Details about the project - category Details about the project - value
Project status: Ongoing
Project manager: Tron Krosshaug
Coworker(s): Roald Bahr, Chris Richter, Andy Franklyn-Miller

Description

Intro: Cutting biomechanics is likely to influence ACL injury risk. However, we do not know which types of cutting techniques that involves higher risk.

Aim: To identify possible clusters of cutting techniques that may be associated with increased risk of ACL injury.

Method: We measured 3D kinetics and kinematics during sport-specific sidestep cutting maneuvers in 776 female elite handball and football players. Players performed sport-specific cutting maneuvers while 3D kinetics and kinematics was captured. We measured full body biomechanics and extracted key variables, characterizing player movements as well as joint loading. We will use standard clustering techniques in machine learning to describe possible clusters of cutting techniques that may be associated with increased risk of ACL injury.