A need for speed: Objectively identifying full-body kinematic and neuromuscular features associated with faster...

Sprinting is multifactorial and dependent on a variety of kinematic, kinetic, and neuromuscular features. A key objective in sprinting is covering a set amount of distance in the shortest amount of time. To achieve this, sprinters are required to coordinate their entire body to achieve a fast sprint velocity. This suggests that a whole-body kinematic and neuromuscular coordinative strategy exists which is associated with improved sprint performance. The purpose of this study was to leverage inertial measurement units (IMUs) and wireless surface electromyography (sEMG) to find coordinative strategies associated with peak over-ground sprint velocity using machine learning. We recruited 40 healthy university age sprint-based athletes from a variety of athletic backgrounds. IMU and sEMG data were used as inputs into a principal components analysis (PCA) to observe major modes of variation (i.e., PC scores). PC scores were then used as inputs into a stepwise multivariate linear regression model to derive associations of each mode of variation with peak sprint velocity. Both the kinematic (R ²= 0.795) and sEMG data (R ²= 0.586) produced significant multivariate linear regression models. The PCs that were selected as inputs into the multivariate linear regression model were reconstructed using multi-component reconstruction to produce a representation of the whole-body movement pattern and changes in the sEMG waveform associated with faster sprint velocities. The findings of this work suggest that distinct features are associated with faster sprint velocity. These include the timing of the contralateral arm and leg swing, stance leg kinematics, dynamic trunk extension at toe-off, asymmetry between the right and left swing side leg and a phase shift feature of the posterior chain musculature. These results demonstrate the utility of data-driven frameworks in identifying different coordinative features that are associated with a movement outcome. Using our framework, coaches and biomechanists can make decisions based on objective movement information, which can ultimately improve an athlete's performance.