Comparing three generic musculoskeletal models to estimate the tibiofemoral reaction forces during gait and sit-to-stand tasks

Transfer of patients' tibiofemoral kinematics and loads to a six-degree-of-freedom (6-DOF) joint simulator under consideration of virtual ligaments

Preclinical testing of total knee replacements (TKR) is crucial for evaluating new implant designs. Dynamic experimental testing focus mostly on level walking and squats, failing to represent a full range of daily activities. Moreover, the contribution of the ligament apparatus is often simplified. Therefore, this study transferred five daily activity load cases-level walking, downhill walking, stair descent, squat, and sit-to-stand-onto a six-degree-of-freedom (6-DOF) joint simulator with a cruciate-retaining bicondylar TKR and a virtual ligament apparatus. Forces and kinematics were based on telemetric data from an ultra-congruent TKR. The resulting kinematics, kinetics, and tibiofemoral contact surfaces were evaluated. Additionally, variations of the virtual ligament apparatus on the joint simulator, e.g. resection of the posterior cruciate ligament (PCL), have been used to assess its influence on the resulting joint dynamics. Results showed that tibiofemoral contact area was more influenced by dynamics than kinematics. Virtual PCL resection shifted the tibia posteriorly (up to 3 mm) and increased abduction (up to 0.5°). Different results were seen across all load cases. The exceptions are the squat and sit-to-stand load cases with similar patterns. Thus, cruciate-retaining TKR can be tested using telemetric data from ultra-congruent TKR, aiding in comprehensive evaluations.

Medial and lateral knee contact forces and muscle forces during sit-to-stand in patients one year after unilateral total knee arthroplasty

Understanding how forces are transmitted through the knee after TKA is essential, as it may explain why many patients experience pain or functional limitations during various activities. This study compared knee muscle forces and knee contact forces (KCF) during sit-to-stand in patients one year after unilateral total knee arthroplasty (TKA) with either a medial ball-and-socket (MBS) or posterior stabilized (PS) implant and compared them to a group of similarly healthy aged controls (CTRL). A musculoskeletal model and static optimization estimated lower limb kinematics, knee kinetics, muscle forces, and KCFs. The normalized sit-to-stand cycle was compared among the groups using statistical nonparametric mapping, and peak between-limb differences were compared using discrete statistics. The PS group required greater forward lean during the sit-to-stand task, causing greater spine flexion, posterior pelvic tilt, and decreased hip flexion on the operated limb. PS and MBS groups favoured their non-operated limb, resulting in less range of motion throughout the lower limb, lower knee kinetics, muscle forces, and KCFs on the operated limb. Compared to the controls, the MBS and PS groups had reduced medial compartment KCF. The control group did favour their dominant limb over their non-dominant limb. Post-operative rehabilitation should continue to promote greater use of the operated knee to have more symmetrical loading between operated and non-operated limbs and improve strength and mobility at the hip and ankle joints. One year after surgery, TKA patients remain with reduced muscle forces and KCF on their operated limb during a sit-to-stand task, regardless whether they received an MBS or PS implant.

Effect of fatigue on knee biomechanics during the sidestep cutting manoeuvre: A modelling approach

Loading both lateral and medial compartments is crucial to understanding the effect of muscle fatigue during sidestep cutting. The present study investigated the changes in tibiofemoral contact forces in the medial and lateral compartments and the muscle force contributions during the sidestep-cutting manoeuvre after a handball-specific fatigue protocol. Twenty female handball athletes performed three trials of the sidestep-cutting manoeuvre before (baseline) and after the fatigue protocol. Motion capture and ground reaction forces were measured, and the data were processed in OpenSim. The variables were compared using statistical parametric mapping (SPM), with a significance level of p < 0.05. The results showed a decreased knee flexion angle during fatigue in the early stance phase. In addition, the post-fatigue analysis demonstrated significantly reduced forces in vasti muscles. Similarly, during fatigue, the SPM analysis showed decreased tibiofemoral contact forces in the vertical and anterior directions. Vertical force applied to both medial and lateral condyles demonstrated a significant reduction after the fatigue protocol. These results indicated that forces applied to the tibiofemoral joint were reduced following the fatigue protocol compared to the baseline values. However, no consistent evidence exists that fatigue increases the risk of knee injuries.

On the prediction of tibiofemoral contact forces for healthy individuals and osteoarthritis patients during gait: a comparative study of regression methods

Knee osteoarthritis (OA) is a public health problem affecting millions of people worldwide. The intensity of the tibiofemoral contact forces is related to cartilage degeneration, and so is the importance of quantifying joint loads during daily activities. Although simulation with musculoskeletal models has been used to calculate joint loads, it demands high-cost equipment and a very time-consuming process. This study aimed to evaluate consolidated machine learning algorithms to predict tibiofemoral forces during gait analysis of healthy individuals and knee OA patients. Also, we evaluated three different datasets to train each model, considering different combinations of primary kinematic and kinetic data, and post-processing data. We evaluated 14 patients with severe unilateral knee OA and 14 healthy individuals during 3-5 gait trials. Data were split into 70% and 30% of the samples as training and test data. Test data was independently evaluated considering a mixture of pathological and healthy individuals, and only OA and Control patients. The main results showed that accurate predictions of the tibiofemoral contact forces were achieved using machine learning methods and that the predictions were sensitive to changes in the input data as training. The present study provided insights into the most promising regressions methods to predict knee contact forces representing an important starting point for the broader application of biomechanical analysis in clinical environments.