Definition of High-Risk Motion Patterns for Female ACL Injury Based on Football-Specific Field Data: A Wearable Sensors Plus Data Mining Approach

Abstract

The aim of the present study was to investigate if the presence of anterior cruciate ligament (ACL) injury risk factors depicted in the laboratory would reflect at-risk patterns in football-specific field data. Twenty-four female footballers (14.9 ± 0.9 year) performed unanticipated cutting maneuvers in a laboratory setting and on the football pitch during football-specific exercises (F-EX) and games (F-GAME). Knee joint moments were collected in the laboratory and grouped using hierarchical agglomerative clustering. The clusters were used to investigate the kinematics collected on field through wearable sensors. Three clusters emerged: Cluster 1 presented the lowest knee moments; Cluster 2 presented high knee extension but low knee abduction and rotation moments; Cluster 3 presented the highest knee abduction, extension, and external rotation moments. In F-EX, greater knee abduction angles were found in Cluster 2 and 3 compared to Cluster 1 (p = 0.007). Cluster 2 showed the lowest knee and hip flexion angles (p < 0.013). Cluster 3 showed the greatest hip external rotation angles (p = 0.006). In F-GAME, Cluster 3 presented the greatest knee external rotation and lowest knee flexion angles (p = 0.003). Clinically relevant differences towards ACL injury identified in the laboratory reflected at-risk patterns only in part when cutting on the field: in the field, low-risk players exhibited similar kinematic patterns as the high-risk players. Therefore, in-lab injury risk screening may lack ecological validity.

Application of Machine Learning Methods to Investigate Joint Load in Agility on the Football Field: Creating the Model, Part I

“…This allows us to move away from in-lab injury risk screening. This is essential, as the information we receive from such constrained types of testing in sterile laboratory environments is not valid [ 23 ]. Previously, we have stated that the adoption of ecological testing will enable football coaching and medical staff to effectively reduce the occurrence of high-risk biomechanical patterns [ 15 ].…”

“…Additionally, ML could be used to predict the likelihood of an athlete suffering an ACL injury based on their movement patterns and other factors, allowing for proactive measures to be taken to prevent ACL injury. By developing player profiles [ 23 ] and classifying players into high versus low KAM during agility, we will work towards a final application, embracing technology that is right around the corner.…”

“…The players executed unanticipated sidestep cutting movements as described earlier [ 15 , 23 ]. In short, the players used a 5 m approach run followed by a 1 foot landing with the preferred leg, and a 40–50° change in direction; this was followed by running through a gate 5 m away.…”

“…Kinetic data concerning the knee in all three directions were extracted and filtered with a fourth-order zero-lag Butterworth low-pass filter at 10 Hz [ 23 ]. The external knee moments were normalized to body mass.…”

Development of a Cutting Technique Modification Training Program and Evaluation of its Effects on Movement Quality and Cutting Performance in Male Adolescent American Football Players

“…Thus, tests under unplanned conditions are likely to be more representative of most sporting and injury situations where the athlete has tight time constraints when responding to a stimulus. Furthermore, the cutting kinematics obtained using on-field and in-lab environments might differ [ 158 , 159 ]. Therefore, when evaluating the effectiveness of a technique modification program, it is recommended that both preplanned and unplanned sidestepping tasks are performed, preferably in a representative on-field environment [ 119 , 158 , 159 ].…”