Electromyography-Driven Forward Dynamics Simulation to Estimate In Vivo Joint Contact Forces During Normal, Smooth, and Bouncy Gaits

Prediction of medial knee contact force using multisource fusion recurrent neural network and transfer learning

Estimation of knee contact force (KCF) during gait provides essential information to evaluate knee joint function. Machine learning has been employed to estimate KCF because of the advantages of low computational cost and real-time. However, the existing machine learning models do not adequately consider gait-related data's temporal-dependent, multidimensional, and highly heterogeneous nature. This study is aimed at developing a multisource fusion recurrent neural network to predict the medial condyle KCF. First, a multisource fusion long short-term memory (MF-LSTM) model was established. Then, we developed a transfer learning strategy based on the MF-LSTM model for subject-specific medial KCF prediction. Four subjects with instrumented tibial prostheses were obtained from the literature. The results showed that the MF-LSTM model could predict medial KCF to a certain high level of accuracy (the mean of ρ = 0.970). The transfer learning model improved the prediction accuracy (the mean of ρ = 0.987). This study shows that the MF-LSTM model is a powerful and accurate computational tool for medial KCF prediction. Introducing transfer learning techniques could further improve the prediction performance for the target subject. This coupling strategy can help clinicians accurately estimate and track joint contact forces in real time.

Simultaneous Evaluation of Tibiofemoral and Patellofemoral Mechanics in Total Knee Arthroplasty: A Combined Experimental and Computational Approach

Contemporary total knee arthroplasty (TKA) has not fully restored natural patellofemoral (P-F) mechanics across the patient population. Previous experimental simulations have been limited in their ability to create dynamic, unconstrained, muscle-driven P-F articulation while simultaneously controlling tibiofemoral (T-F) contact mechanics. The purpose of this study was to develop a novel experimental simulation and validate a corresponding finite element model to evaluate T-F and P-F mechanics. A commercially available wear simulator was retrofitted with custom fixturing to evaluate whole-knee TKA mechanics with varying patella heights during a simulated deep knee bend. A corresponding dynamic finite element model was developed to validate kinematic and kinetic predictions against experimental measurements. Patella alta reduced P-F reaction forces in early and midflexion, corresponding with an increase in T-F forces that indicated an increase in extensor mechanism efficiency. Due to reduced wrapping of the extensor mechanism in deeper flexion for the alta condition, peak P-F forces in flexion increased from 101% to 135% of the applied quadriceps load for the baja and alta conditions, respectively. Strong agreement was observed between the experiment and model predictions with root-mean-square errors (RMSE) for P-F kinematics ranging from 0.8 deg to 3.3 deg and 0.7 mm to 1.4 mm. RMSE for P-F forces ranged from 7.4 N to 53.6 N. By simultaneously controlling dynamic, physiological loading of the T-F and P-F joint, this novel experimental simulation and validated model will be a valuable tool for investigation of future TKA designs and surgical techniques.

Can static optimization detect changes in peak medial knee contact forces induced by gait modifications?

Medial knee contact force (MCF) is related to the pathomechanics of medial knee osteoarthritis. However, MCF cannot be directly measured in the native knee, making it difficult for therapeutic gait modifications to target this metric. Static optimization, a musculoskeletal simulation technique, can estimate MCF, but there has been little work validating its ability to detect changes in MCF induced by gait modifications. In this study, we quantified the error in MCF estimates from static optimization compared to measurements from instrumented knee replacements during normal walking and seven different gait modifications. We then identified minimum magnitudes of simulated MCF changes for which static optimization correctly identified the direction of change (i.e., whether MCF increased or decreased) at least 70% of the time. A full-body musculoskeletal model with a multi-compartment knee and static optimization was used to estimate MCF. Simulations were evaluated using experimental data from three subjects with instrumented knee replacements who walked with various gait modifications for a total of 115 steps. Static optimization underpredicted the first peak (mean absolute error = 0.16 bodyweights) and overpredicted the second peak (mean absolute error = 0.31 bodyweights) of MCF. Average root mean square error in MCF over stance phase was 0.32 bodyweights. Static optimization detected the direction of change with at least 70% accuracy for early-stance reductions, late-stance reductions, and early-stance increases in peak MCF of at least 0.10 bodyweights. These results suggest that a static optimization approach accurately detects the direction of change in early-stance medial knee loading, potentially making it a valuable tool for evaluating the biomechanical efficacy of gait modifications for knee osteoarthritis.

Comparison of Azure Kinect and optical retroreflective motion capture for kinematic and spatiotemporal evaluation of the sit-to-stand test

BACKGROUND

The sit-to-stand test (STS) is commonly used to evaluate functional capabilities within a variety of clinical populations. Traditionally STS is a timed test, limiting the depth of information which can be gained from its evaluation. The Azure Kinect has the potential to add in-depth analysis to STS. Despite these potential benefits, the recently released (2019) Azure Kinect has yet to be evaluated for its ability to accurately assess STS.

RESEARCH QUESTIONS

Purposes of this work were to compare data captured during STS using both a 12 camera Vicon motion capture system and the Azure Kinect; and to calculate kinematic and spatiotemporal variables related to the four phases of the STS cycle.

METHODS

Spatiotemporal and kinematic measures for STS were simultaneously collected by both devices for 15 participants. Cycle waveforms were compared for right and left hip and knee flexion/extension angular displacement, right and left hip and knee flexion/extension angular velocity, and knee-to-ankle separation ratio. Evaluated discrete outcome variables included: phase time points, maximum knee extension velocity from phases 3 to 4, medial-lateral pelvic sway range, and total time to completion. Waveform summary data were compared using R, R², and RMSE. Discrete variables were analyzed using Spearman's Rank correlation coefficient.

RESULTS

R and R² values between the two systems indicated high levels of correlation (all R values > 0.711, all R² values > 0.660). Although there was an overall high level of agreement between waveform shapes, high RMSE values indicated some minor tracking errors for Kinect within the STS cycle. Spearman's Rank correlation coefficient indicated high levels of correlation between the systems for discrete variables (all R values > 0.89), with the exception of medial-lateral pelvic sway range.

SIGNIFICANCE

The Azure Kinect provides valuable insight into STS movement strategies allowing for improved precision in clinical decision making across multiple clinical populations.

Deciphering the "Art" in Modeling and Simulation of the Knee Joint: Overall Strategy

Recent explorations of knee biomechanics have benefited from computational modeling, specifically leveraging advancements in finite element analysis and rigid body dynamics of joint and tissue mechanics. A large number of models have emerged with different levels of fidelity in anatomical and mechanical representation. Adapted modeling and simulation processes vary widely, based on justifiable choices in relation to anticipated use of the model. However, there are situations where modelers' decisions seem to be subjective, arbitrary, and difficult to rationalize. Regardless of the basis, these decisions form the "art" of modeling, which impact the conclusions of simulation-based studies on knee function. These decisions may also hinder the reproducibility of models and simulations, impeding their broader use in areas such as clinical decision making and personalized medicine. This document summarizes an ongoing project that aims to capture the modeling and simulation workflow in its entirety-operation procedures, deviations, models, by-products of modeling, simulation results, and comparative evaluations of case studies and applications. The ultimate goal of the project is to delineate the art of a cohort of knee modeling teams through a publicly accessible, transparent approach and begin to unravel the complex array of factors that may lead to a lack of reproducibility. This manuscript outlines our approach along with progress made so far. Potential implications on reproducibility, on science, engineering, and training of modeling and simulation, on modeling standards, and on regulatory affairs are also noted.

Investigation of gait cycle deviation over surface irregularities utilizing muscle activities

BACKGROUND

Human beings regularly walk over even and uneven surfaces during their daily activities. A human being with lower limb disability needs an exoskeleton to walk independently. However, walking surface irregularities increase the risk of falling of exoskeleton users. This falling tendency can be minimized by balancing the exoskeleton on irregular surface profiles against the gait cycle variation. Gait variation is studied using quality EMG signals obtained from the gastrocnemius and hamstring muscle activity during uneven surface walking.

OBJECTIVE

The present study compares the activity of hamstring and gastrocnemius muscles during walking on a treadmill, utilizing both even and uneven planes.

METHODS

Integrated electromyography signals from eight healthy male subjects are collected while walking on a treadmill, even and uneven planes. Muscle activity variation on these planes is studied using two-way ANOVA with replications.

RESULTS

The results show that hamstring muscle activity registers a sound variation in swing phase but has no variation in stance phase over all three planes, whereas gastrocnemius muscle activity changes between swing and stance phases over even and uneven planes during forward walking.

CONCLUSIONS

The results illustrate that the gait cycle variation depends on surface irregularities which indicates the importance of surface consideration.

Contribution of passive actions to the lower limb joint moments and powers during gait: A comparison of models

The lower limb passive actions representing the actions of all the passive periarticular structures have been shown to have a significant contribution to the power generation and absorption during gait. However, the respective magnitude of its different components was not established, although models of ligament moment were implemented in some musculoskeletal models. These ligament moments have shown to have an influence on the musculo-tendon forces and contact forces but the models used were never specifically evaluated, that is, compared to the passive and net joint moments. Two models of passive joint moments and three models of ligament moments were selected from the literature. Ten subjects (23–29 years old, 79.8 ± 9.5 kg, 1.85 ± 0.06 m) participated in the study. Each subject performed three gait cycles in a gait laboratory to acquire the kinematics and ground reaction forces and to compute the ligament, passive and net moments of the right lower limb joints. The contributions of the passive joint moments to the net joint moments were in accordance with the literature, although time shifts appeared for peaks in the hip and knee powers. Two of the models of ligament moments seemed, in fact, to represent the passive joint moments as their contributions were very similar while the third model of ligament moments seemed to represent only penalty-based joint limits. As a conclusion, this study showed that the models of ligament moments existing in the literature do not seem reliable. This study also demonstrated that the use of non-subject-specific models of the passive joint moments could be a valid approach for healthy subjects.