Prediction of Knee Joint Contact Forces From External Measures Using Principal Component Prediction and Reconstruction

Abstract

Abnormal loading of the knee joint contributes to the pathogenesis of knee osteoarthritis. Gait retraining is a noninvasive intervention that aims to reduce knee loads by providing audible, visual, or haptic feedback of gait parameters. The computational expense of joint contact force prediction has limited real-time feedback to surrogate measures of the contact force, such as the knee adduction moment. We developed a method to predict knee joint contact forces using motion analysis and a statistical regression model that can be implemented in near real-time. Gait waveform variables were deconstructed using principal component analysis, and a linear regression was used to predict the principal component scores of the contact force waveforms. Knee joint contact force waveforms were reconstructed using the predicted scores. We tested our method using a heterogenous population of asymptomatic controls and subjects with knee osteoarthritis. The reconstructed contact force waveforms had mean (SD) root mean square differences of 0.17 (0.05) bodyweight compared with the contact forces predicted by a musculoskeletal model. Our method successfully predicted subject-specific shape features of contact force waveforms and is a potentially powerful tool in biofeedback and clinical gait analysis.

Statistical Modeling of Lower Limb Kinetics During Deep Squat and Forward Lunge

“…Using this for a statistical model, it enables to generate population data from a small set of clinical data. The kinetic model should represent waveform data as a linear combination of vectors, representing the primary modes of variation in experimental data (Jolliffe et al, 2002;Saliba et al, 2018). Eigenvalues and eigenvectors have been created by singular value decomposition.…”