Presentation Session II

Post-ACLR runners often display biomechanical asymmetries in running, heightening re-injury risks. This project employs functional Principal Component Analysis (fPCA) for dimension reduction on 101-point time-normalized waveforms, including joint angles, moments, velocities for hip/knee/ankle, GRF in three planes, and COM data. Reduced features are clustered using K-means, Gaussian Mixture Models (GMM), and DBSCAN, with comparisons via internal validity metrics like silhouette scores and Davies-Bouldin indices. By identifying modifiable deficits, it supports tailored rehabilitation programs, potentially reducing re-injury rates and improving long-term outcomes.