Fine Tuning Total Knee Replacement Contact Force Prediction Algorithms Using Blinded Model Validation

Abstract

The purpose of this study was to perform a blinded comparison of model predictions of total knee replacement contact forces to in vivo forces from an instrumented prosthesis during normal walking and medial thrust gait by participating in the “Third Grand Challenge Competition to Predict in vivo Knee Loads.” We also evaluated model assumptions that were critical for accurate force predictions. Medial, lateral, and total axial forces through the knee were calculated using a previously developed and validated parametric numerical model. The model uses equilibrium equations between internal and external moments and forces to obtain knee joint contact forces and calculates a range of forces at instances during the gait cycle through parametric variation of muscle activity levels. For 100 instances during a normal over-ground gait cycle, model root mean square differences from eTibia data were 292, 248, and 281 for medial, lateral, and total contact forces, respectively. For 100 instances during a medial thrust gait cycle, model root mean square differences from eTibia data were 332, 234, and 470 for medial, lateral, and total contact forces, respectively. The percent difference between measured and predicted peak total axial force was 2.89% at the first peak and 9.36% at the second peak contact force for normal walking and 3.94% at the first peak and 14.86% at the second peak contact force for medial thrust gait. After unblinding, changes to model assumptions improved medial and lateral force predictions for both gait styles but did not improve total force predictions. Axial forces computed with the model compared well to the eTibia data under blinded and unblinded conditions. Knowledge of detailed knee kinematics, namely anterior-posterior translation, appears to be critical in obtaining accurate force predictions.

Muscle Synergies May Improve Optimization Prediction of Knee Contact Forces During Walking

“…The medial and lateral contact force predictions in our study were as good as or better than predictions generated by other recent studies that used the Knee Grand Challenge data sets [40][41][42][43][44]. Our best knee contact force predictions were for the CalibrateþPredict case when synergy controls were used without EMG shape tracking.…”

Musculoskeletal Simulation for Assessment of Effect of Movement-Based Structure-Modifying Treatment Strategies

“…Neural motor control and its resulting joint kinematics and muscle forces provide basic knowledge of locomotion function of the human body in the normal as well as in the pathophysiological conditions [16,33,34]. Moreover, the better understanding of this complex underlying mechanism allows a better elucidation of the mechanisms behind body growth, aging progression, and disease development [3,23].…”

A Hertzian Integrated Contact Model of the Total Knee Replacement Implant for the Estimation of Joint Contact Forces

“…Regarding the Knee Grand Challenge, a series of editions has been organized and many efforts have been investigated to predict the in vivo contact forces using different approaches [15,[21][22][23][24][25][26]. In fact, contact forces may be predicted with or without a contact model formulation.…”

“…In fact, contact forces may be predicted with or without a contact model formulation. Even if the approach without a contact model revealed interesting results [26], this approach is based purely on the numerical power to find the statistical correlation pattern between the contact forces and other physically measurable or computable quantities such as EMG-based muscle activation levels or maximum physiological muscle moments. Besides, the use of a contact model may provide more descriptive and meaningful information inside the implant-to-implant interaction under musculoskeletal load.…”

“…Moreover, Electromyography-(EMG-) driven muscle force estimation approach coupled with moment balancing algorithm has been used to predict the medial contact force [24,25]. Furthermore, a parametric numerical model including 9 equilibrium equations reflecting muscle activation levels, passive structure activation, joint moments, external joint forces, maximum physiological muscle moments, and forces has been also developed to predict the knee contact forces [26]. In fact, without contact model, the problem formulation and computational cost may be more advantageous than the use of a contact model.…”