Changes in in vivo knee contact forces through gait modification

Predicting Knee Joint Contact Forces During Normal Walking Using Kinematic Inputs With a Long-Short Term Neural Network

Knee joint contact forces are commonly estimated via surrogate measures (i.e., external knee adduction moments or musculoskeletal modeling). Despite its capabilities, modeling is not optimal for clinicians or persons with limited experience. The purpose of this study was to design a novel prediction method for knee joint contact forces that is simplistic in terms of required inputs. This study included marker trajectories and instrumented knee forces during normal walking from the "Grand Challenge" (n = 6) and "CAMS" (n = 2) datasets. Inverse kinematics were used to derive stance phase hip (sagittal, frontal, transverse), knee (sagittal, frontal), ankle (sagittal), and trunk (frontal) kinematics. A long-short term memory network (LSTM) was created using matlab to predict medial and lateral knee force waveforms using combinations of the kinematics. The Grand Challenge and CAMS datasets trained and tested the network, respectively. Musculoskeletal modeling forces were derived using static optimization and joint reaction tools in OpenSim. Waveform accuracy was determined as the proportion of variance and root-mean-square error between network predictions and in vivo data. The LSTM network was highly accurate for medial forces (R2 = 0.77, RMSE = 0.27 BW) and required only frontal hip and knee and sagittal hip and ankle kinematics. Modeled medial force predictions were excellent (R2 = 0.77, RMSE = 0.33 BW). Lateral force predictions were poor for both methods (LSTM R2 = 0.18, RMSE = 0.08 BW; modeling R2 = 0.21, RMSE = 0.54 BW). The designed LSTM network outperformed most reports of musculoskeletal modeling, including those reached in this study, revealing knee joint forces can accurately be predicted by using only kinematic input variables.

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.

Simulated Tibiofemoral Joint Reaction Forces for Three Previously Studied Gait Modifications in Healthy Controls

Gait modifications, such as lateral trunk lean (LTL), medial knee thrust (MKT), and toe-in gait (TIG), are frequently investigated interventions used to slow the progression of knee osteoarthritis. The Lerner knee model was developed to estimate the tibiofemoral joint reaction forces (JRF) in the medial and lateral compartments during gait. These models may be useful for estimating the effects on the JRF in the knee as a result of gait modifications. We hypothesized that all gait modifications would decrease the JRF compared to normal gait. Twenty healthy individuals volunteered for this study (26.7 ± 4.7 years, 1.75 ± 0.1 m, 73.4 ± 12.4 kg). Ten trials were collected for normal gait as well as for the three gait modifications: LTL, MKT, and TIG. The data were used to estimate the JRF in the first and second peaks for the medial and lateral compartments of the knee via opensim using the Lerner knee model. No significant difference from baseline was found for the first peak in the medial compartment. There was a decrease in JRF in the medial compartment during the loading phase of gait for TIG (6.6%) and LTL (4.9%) and an increasing JRF for MKT (2.6%). but none was statistically significant. A significant increase from baseline was found for TIG (5.8%) in the medial second peak. We found a large variation in individual responses to gait interventions, which may help explain the lack of statistically significant results. Possible factors influencing these wide ranges of responses to gait modifications include static alignment and the impacts of variation in muscle coordination strategies used, by participants, to implement gait modifications.

Prediction of in vivo hip contact forces during common activities of daily living using a segment-based musculoskeletal model

Background: Quantifying in vivo hip muscle and contact forces during activities of daily living (ADL) provides valuable information for diagnosis and treatment of hip-related disorders. The objective of this study was to utilize Freebody, a segment-based musculoskeletal model, for the prediction of hip contact forces using a novel objective function during seven common ADLs and validate its performance against the publicly available HIP98 dataset. Methods: Marker data, ground reaction forces, and hip contact forces during slow, normal, and fast walking, stair ascent and descent, and standing up and sitting down were extracted for 3 subjects from the HIP98 dataset. A musculoskeletal anatomical dataset was scaled to match the dimensions of each subject, and muscle and hip contact forces were estimated by minimizing a novel objective function, which was the summation of the muscle stresses squared and body weight-normalised hip contact force. The accuracy of predictions were quantified using several metrics, and muscle forces were qualitatively compared to experimental EMGs in the literature. Results: FreeBody predicted the hip contact forces during the ADLs with encouraging accuracy: The root mean squared error of predictions were 44.0 ± 8.5, 47.4 ± 6.5, and 59.8 ± 7.1% BW during slow, normal, and fast walking, 44.2 ± 16.8% and 53.3 ± 12.2% BW for stair ascent and descent, and 31.8 ± 8.2% and 17.1 ± 5.0% BW for standing up and sitting down, respectively. The error in prediction of peak hip contact forces were 14-18%, 24-28%, 17-35% for slow, normal, and fast walking, 7-25% and 15-32% in stair ascent and descent, and around 10% for standing up and sitting down. The coefficient of determination was larger than 0.90 in all activities except in standing up (0.86 ± 0.08). Conclusion: This study has implemented a novel objective function in a segment-based musculoskeletal model, FreeBody, for the prediction of hip contact forces during a large range of ADLs. The model outputs compare favourably for all ADLs and are the best in standing up and sitting down, while muscle activation patterns are consistent with experimental EMGs from literature. This new objective function addresses one of the major limitations associated with musculoskeletal models in the literature, namely the high non-physiological predicted hip joint contact forces.

Muscle coordination retraining inspired by musculoskeletal simulations reduces knee contact force

Humans typically coordinate their muscles to meet movement objectives like minimizing energy expenditure. In the presence of pathology, new objectives gain importance, like reducing loading in an osteoarthritic joint, but people often do not change their muscle coordination patterns to meet these new objectives. Here we use musculoskeletal simulations to identify simple changes in coordination that can be taught using electromyographic biofeedback, achieving the therapeutic goal of reducing joint loading. Our simulations predicted that changing the relative activation of two redundant ankle plantarflexor muscles—the gastrocnemius and soleus—could reduce knee contact force during walking, but it was unclear whether humans could re-coordinate redundant muscles during a complex task like walking. Our experiments showed that after a single session of walking with biofeedback of summary measures of plantarflexor muscle activation, healthy individuals reduced the ratio of gastrocnemius-to-soleus muscle activation by 25 ± 15% (p = 0.004, paired t test, n = 10). Participants who walked with this “gastrocnemius avoidance” gait pattern reduced late-stance knee contact force by 12 ± 12% (p = 0.029, paired t test, n = 8). Simulation-informed coordination retraining could be a promising treatment for knee osteoarthritis and a powerful tool for optimizing coordination for a variety of rehabilitation and performance applications.

Walking Pole Gait to Reduce Joint Loading post Total Knee Athroplasty: Musculoskeletal modeling Approach

Excessive knee contact loading is precursor to osteoarthritis and related knee ailment leading to knee athroplasty. Reducing contact loading through gait modifications using assisted pole walking offers noninvasive process of medial load offloading at knee joint. In this paper, we evaluate the efficacy of different configuration of pole walking for reducing contact force at the knee joint through musculoskeletal (MSK) modeling. We have developed a musculoskeletal model for a subject with knee athroplasty utilizing in-vivo implant data and computed tibio-femoral contact force for different pole walking conditions to evaluate the best possible configuration for guiding rehabilitation, correlated with different gait phases. Effect of gait speed variation on knee contact force, hip joint dynamics and muscle forces are simulated using the developed MSK model. Results indicate some interesting trend of load reduction, dependent on loading phases pertaining to different pole configuration. Insights gained from the simulation can aid in designing personalized rehabilitation therapy for subjects suffering from Osteoarthritis.

The effects of knee pain on knee contact force and external knee adduction moment in patients with knee osteoarthritis

Knee osteoarthritis (OA) is a major cause of knee pain, leading to physical dysfunction. External knee adduction moment (KAM), a surrogate measure of knee contact force (KCF) in the medial compartment, is related to knee pain, but the association between KCF and pain severity remains unclear. This study aimed to reveal the differences in KCF due to pain severity. Twenty-eight patients with knee OA were evaluated knee symptoms including pain severity via the Knee Society Score. Based on the median symptom score, 17 points in this study, subjects were classified as having Mild symptomatic OA (n = 15) and Severe symptomatic OA (n = 13). Subjects walked three times at a comfortable speed along a six-meter walkway, and we calculated KAM during the stance phase. KCF magnitude and distribution were also computed using the subject-specific musculoskeletal model, considering physical characteristics such as the femorotibial angle measured by X-ray. No differences in physical characteristics such as femorotibial angle and gait speed were found by symptom severity, whereas KAM and medial KCF at minimum and second peak in Severe symptomatic OA patients were significantly greater than those in Mild symptomatic OA. A significant medial shift of KCF in Severe symptomatic OA was also seen at first peak and minimum. Severe symptomatic OA had a greater medial KCF and medial shift of KCF. Detailed evaluations of KCF magnitude and distribution in addition to KAM would provide crucial information on knee contact force in relation to symptom severity.

Knee loading in OA subjects is correlated to flexion and adduction moments and to contact point locations

This study evaluated the association of contact point locations with the knee medial and lateral contact force (F_med, F_lat) alterations in OA and healthy subjects. A musculoskeletal model of the lower limb with subject-specific tibiofemoral contact point trajectories was used to estimate the F_med and F_lat in ten healthy and twelve OA subjects during treadmill gait. Regression analyses were performed to evaluate the correlation of the contact point locations, knee adduction moment (KAM), knee flexion moment (KFM), frontal plane alignment, and gait speed with the F_med and F_lat. Medial contact point locations in the medial–lateral direction showed a poor correlation with the F_med in OA (R² = 0.13, p = 0.01) and healthy (R² = 0.24, p = 0.001) subjects. Anterior–posterior location of the contact points also showed a poor correlation with the F_med of OA subjects (R² = 0.32, p < 0.001). Across all subjects, KAM and KFM remained the best predictors of the F_med and F_lat, respectively (R² between 0.62 and 0.69). Results suggest different mechanisms of contact force distribution in OA joints. The variations in the location of the contact points participate partially to explains the F_med variations in OA subjects together with the KFM and KAM.

A Systematic Review of the Associations Between Inverse Dynamics and Musculoskeletal Modeling to Investigate Joint Loading in a Clinical Environment

The assessment of knee or hip joint loading by external joint moments is mainly used to draw conclusions on clinical decision making. However, the correlation between internal and external loads has not been systematically analyzed. This systematic review aims, therefore, to clarify the relationship between external and internal joint loading measures during gait. A systematic database search was performed to identify appropriate studies for inclusion. In total, 4,554 articles were identified, while 17 articles were finally included in data extraction. External joint loading parameters were calculated using the inverse dynamics approach and internal joint loading parameters by musculoskeletal modeling or instrumented prosthesis. It was found that the medial and total knee joint contact forces as well as hip joint contact forces in the first half of stance can be well predicted using external joint moments in the frontal plane, which is further improved by including the sagittal joint moment. Worse correlations were found for the peak in the second half of stance as well as for internal lateral knee joint contact forces. The estimation of external joint moments is useful for a general statement about the peak in the first half of stance or for the maximal loading. Nevertheless, when investigating diseases as valgus malalignment, the estimation of lateral knee joint contact forces is necessary for clinical decision making because external joint moments could not predict the lateral knee joint loading sufficient enough. Dependent on the clinical question, either estimating the external joint moments by inverse dynamics or internal joint contact forces by musculoskeletal modeling should be used.

Real-Time Prediction of Joint Forces by Motion Capture and Machine Learning

Conventional biomechanical modelling approaches involve the solution of large systems of equations that encode the complex mathematical representation of human motion and skeletal structure. To improve stability and computational speed, being a common bottleneck in current approaches, we apply machine learning to train surrogate models and to predict in near real-time, previously calculated medial and lateral knee contact forces (KCFs) of 54 young and elderly participants during treadmill walking in a speed range of 3 to 7 km/h. Predictions are obtained by fusing optical motion capture and musculoskeletal modeling-derived kinematic and force variables, into regression models using artificial neural networks (ANNs) and support vector regression (SVR). Training schemes included either data from all subjects (LeaveTrialsOut) or only from a portion of them (LeaveSubjectsOut), in combination with inclusion of ground reaction forces (GRFs) in the dataset or not. Results identify ANNs as the best-performing predictor of KCFs, both in terms of Pearson R (0.89-0.98 for LeaveTrialsOut and 0.45-0.85 for LeaveSubjectsOut) and percentage normalized root mean square error (0.67-2.35 for LeaveTrialsOut and 1.6-5.39 for LeaveSubjectsOut). When GRFs were omitted from the dataset, no substantial decrease in prediction power of both models was observed. Our findings showcase the strength of ANNs to predict simultaneously multi-component KCF during walking at different speeds-even in the absence of GRFs-particularly applicable in real-time applications that make use of knee loading conditions to guide and treat patients.

Random Forest enhancement using improved Artificial Fish Swarm for the medial knee contact force prediction

Knee contact force (KCF) is an important factor to evaluate the knee joint function for the patients with knee joint impairment. However, the KCF measurement based on the instrumented prosthetic implants or inverse dynamics analysis is limited due to the invasive, expensive price and time consumption. In this work, we propose a KCF prediction method by integrating the Artificial Fish Swarm and the Random Forest algorithm. First, we train a Random Forest to learn the nonlinear relation between gait parameters (input) and contact pressures (output) based on a dataset of three patients instrumented with knee replacement. Then, we use the improved artificial fish group algorithm to optimize the main parameters of the Random Forest based KCF prediction model. The extensive experiments verify that our method can predict the medial knee contact force both before and after the intervention of gait patterns, and the performance outperforms the classical multi-body dynamics analysis and artificial neural network model.

Extra excitation of biceps femoris during neuromuscular electrical stimulation reduces knee medial loading

Medial knee joint osteoarthritis (OA) is a debilitating and prevalent condition. Surgical treatment consists of redistributing the forces from the medial to the lateral compartment through osteotomy, or replacing the joint surfaces. As the mediolateral load distribution is related to the action of the musculature around the knee, the aim of this study was to devise a technique to redistribute these forces non-surgically through changes in muscle excitation. Eight healthy subjects participated in the experiment, and neuromuscular electrical stimulation was used to change the muscle forces around the knee. A musculoskeletal model was used to quantify the loading on the medial compartment of the knee, and a novel algorithm devised and implemented to simulate neuromuscular electrical stimulation. The forces and moments at the knee, ground reaction forces, walking velocity and step length were quantified before and after stimulation. Stimulation of the biceps femoris resulted in a significant decrease in the second peak of the medial knee joint loading by up to 0.17 body weight (p = 0.016). Kinematic parameters were not significantly affected. Neuromuscular electrical stimulation can decrease the peak loads on the medial compartment of the knee, and thus offers a promising therapy for medial knee joint OA.

Relationship between knee joint contact forces and external knee joint moments in patients with medial knee osteoarthritis: effects of gait modifications

OBJECTIVE

To evaluate 1) the relationship between the knee contact force (KCF) and knee adduction and flexion moments (KAM and KFM) during normal gait in people with medial knee osteoarthritis (KOA), 2) the effects on the KCF of walking with a modified gait pattern and 3) the relationship between changes in the KCF and changes in the knee moments.

METHOD

We modeled the gait biomechanics of thirty-five patients with medial KOA using the AnyBody Modeling System during normal gait and two modified gait patterns. We calculated the internal KCF and evaluated the external joint moments (KAM and KFM) against it using linear regression analyses.

RESULTS

First peak medial KCF was associated with first peak KAM (R² = 0.60) and with KAM and KFM (R² = 0.73). Walking with both modified gait patterns reduced KAM (P = 0.002) and the medial to total KCF ratio (P < 0.001) at the first peak. Changes in KAM during modified gait were moderately associated with changes in the medial KCF at the first peak (R² = 0.54 and 0.53).

CONCLUSIONS

At the first peak, KAM is a reasonable substitute for the medial contact force, but not at the second peak. First peak KFM is also a significant contributor to the medial KCF. At the first peak, walking with a modified gait reduced the ratio of the medial to total KCF but not the medial KCF itself. To determine the effects of gait modifications on cartilage loading and disease progression, longitudinal studies and individualized modeling, accounting for motion control, would be required.

Biofeedback for Gait Retraining Based on Real-Time Estimation of Tibiofemoral Joint Contact Forces

Biofeedback assisted rehabilitation and intervention technologies have the potential to modify clinically relevant biomechanics. Gait retraining has been used to reduce the knee adduction moment, a surrogate of medial tibiofemoral joint loading often used in knee osteoarthritis research. In this paper, we present an electromyogram-driven neuromusculoskeletal model of the lower-limb to estimate, in real-time, the tibiofemoral joint loads. The model included 34 musculotendon units spanning the hip, knee, and ankle joints. Full-body inverse kinematics, inverse dynamics, and musculotendon kinematics were solved in real-time from motion capture and force plate data to estimate the knee medial tibiofemoral contact force (MTFF). We analyzed five healthy subjects while they were walking on an instrumented treadmill with visual biofeedback of their MTFF. Each subject was asked to modify their gait in order to vary the magnitude of their MTFF. All subjects were able to increase their MTFF, whereas only three subjects could decrease it, and only after receiving verbal suggestions about possible gait modification strategies. Results indicate the important role of knee muscle activation patterns in modulating the MTFF. While this paper focused on the knee, the technology can be extended to examine the musculoskeletal tissue loads at different sites of the human body.

Neural Data-Driven Musculoskeletal Modeling for Personalized Neurorehabilitation Technologies

OBJECTIVES

The development of neurorehabilitation technologies requires the profound understanding of the mechanisms underlying an individual's motor ability and impairment. A major factor limiting this understanding is the difficulty of bridging between events taking place at the neurophysiologic level (i.e., motor neuron firings) with those emerging at the musculoskeletal level (i.e. joint actuation), in vivo in the intact moving human. This review presents emerging model-based methodologies for filling this gap that are promising for developing clinically viable technologies.

METHODS

We provide a design overview of musculoskeletal modeling formulations driven by recordings of neuromuscular activity with a critical comparison to alternative model-free approaches in the context of neurorehabilitation technologies. We present advanced electromyography-based techniques for interfacing with the human nervous system and model-based techniques for translating the extracted neural information into estimates of motor function.

RESULTS

We introduce representative application areas where modeling is relevant for accessing neuromuscular variables that could not be measured experimentally. We then show how these variables are used for designing personalized rehabilitation interventions, biologically inspired limbs, and human-machine interfaces.

CONCLUSION

The ability of using electrophysiological recordings to inform biomechanical models enables accessing a broader range of neuromechanical variables than analyzing electrophysiological data or movement data individually. This enables understanding the neuromechanical interplay underlying in vivo movement function, pathology, and robot-assisted motor recovery.

SIGNIFICANCE

Filling the gap between our understandings of movement neural and mechanical functions is central for addressing one of the major challenges in neurorehabilitation: personalizing current technologies and interventions to an individual's anatomy and impairment.

Reductions in knee joint forces with weight loss are attenuated by gait adaptations in class III obesity

A consensus exists that high knee joint forces are a precursor to knee osteoarthritis and weight loss reduces these forces. Because large weight loss also leads to increased step length and walking velocity, knee contact forces may be reduced less than predicted by the magnitude of weight loss. The purpose was to determine the effects of weight loss on knee muscle and joint loads during walking in Class III obese adults. We determined through motion capture, force platform measures and biomechanical modeling the effects of weight loss produced by gastric bypass surgery over one year on knee muscle and joint loads during walking at a standard, controlled velocity and at self-selected walking velocities. Weight loss equaling 412 N or 34% of initial body weight reduced maximum knee compressive force by 824 N or 67% of initial body weight when walking at the controlled velocity. These changes represent a 2:1 reduction in knee force relative to weight loss when walking velocity is constrained to the baseline value. However, behavioral adaptations including increased stride length and walking velocity in the self-selected velocity condition attenuated this effect by ∼50% leading to a 392 N or 32% initial body weight reduction in compressive force in the knee joint. Thus, unconstrained walking elicited approximately 1:1 ratio of reduction in knee force relative to weight loss and is more indicative of walking behavior than the standard velocity condition. In conclusion, massive weight loss produces dramatic reductions in knee forces during walking but when patients stride out and walk faster, these favorable reductions become substantially attenuated.

In Vivo Knee Contact Force Prediction Using Patient-Specific Musculoskeletal Geometry in a Segment-Based Computational Model

Segment-based musculoskeletal models allow the prediction of muscle, ligament, and joint forces without making assumptions regarding joint degrees-of-freedom (DOF). The dataset published for the “Grand Challenge Competition to Predict in vivo Knee Loads” provides directly measured tibiofemoral contact forces for activities of daily living (ADL). For the Sixth Grand Challenge Competition to Predict in vivo Knee Loads, blinded results for “smooth” and “bouncy” gait trials were predicted using a customized patient-specific musculoskeletal model. For an unblinded comparison, the following modifications were made to improve the predictions: further customizations, including modifications to the knee center of rotation; reductions to the maximum allowable muscle forces to represent known loss of strength in knee arthroplasty patients; and a kinematic constraint to the hip joint to address the sensitivity of the segment-based approach to motion tracking artifact. For validation, the improved model was applied to normal gait, squat, and sit-to-stand for three subjects. Comparisons of the predictions with measured contact forces showed that segment-based musculoskeletal models using patient-specific input data can estimate tibiofemoral contact forces with root mean square errors (RMSEs) of 0.48–0.65 times body weight (BW) for normal gait trials. Comparisons between measured and predicted tibiofemoral contact forces yielded an average coefficient of determination of 0.81 and RMSEs of 0.46–1.01 times BW for squatting and 0.70–0.99 times BW for sit-to-stand tasks. This is comparable to the best validations in the literature using alternative models.

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

The better understanding of the complex mechanism between neural motor control and its resulting joint kinematics and muscle forces allows a better elucidation of the mechanisms behind body growth, aging progression, and disease development. This study aimed at investigating the impact of movement-based structure-modifying treatment strategies on joint kinematics, muscle forces, and muscle synergies of the gait with instrumented implant. A patient-specific musculoskeletal model was used to quantitatively assess the deviations of joint and muscle behaviors between the normal gait and 4 gait modifications (bouncy, medial thrust, midcrouch, and mtp (i.e., gait with forefoot strike)). Moreover, muscle synergy analysis was performed using EMG-based nonnegative matrix factorization. Large variation of 19 degrees and 190 N was found for knee flexion/extension and lower limb muscle forces, respectively. EMG-based muscle synergy analysis revealed that the activation levels of the vastus lateralis and tibialis anterior are dominant for the midcrouch gait. In addition, an important contribution of semimembranosus to the medial thrust and midcrouch gaits was also observed. In fact, such useful information could allow a better understanding of the joint function and muscle synergy strategies leading to deeper knowledge of joint and muscle mechanisms related to neural voluntary motor commands.

Calculation of external knee adduction moments: a comparison of an inverse dynamics approach and a simplified lever-arm approach

BACKGROUND

The external knee adduction moment (EKAM) is often studied in knee osteoarthritis research. This study compared EKAMs between two methods of calculation: a method that only requires ground reaction force and knee position data (i.e. lever-arm), and an inverse dynamics link-segment method.

METHODS

Sixteen participants walked while wearing a control shoe with and without a six millimeter lateral wedge insole. Peak EKAMs between the lever-arm and inverse dynamics methods were compared for the control condition, and the %change in moment induced by the lateral wedge was compared between methods.

RESULTS

When comparing EKAMs between methods, no correlation was found (r=0.24, p=0.36); peak EKAMs with the lever-arm method (26.0Nm) were significantly lower than EKAMs with the inverse dynamics method (40.2Nm, pb0.001); and Bland-Altman plots showed poor agreement between methods. When assessing the %change in moment with a lateral wedge, a moderate correlation was found (r=0.55, p=0.03) between methods; Bland-Altman plots showed moderate agreement between methods; and the lever-arm method (-6.4%) was not significantly different from the inverse dynamics method (-11.4%, p=0.09); however, the two methods produced opposite results 31% of the time.

CONCLUSION

The lever-arm method cannot estimate peak EKAMs, and can only approximate the %change in moment induced by a lateral wedge; however, the error rate was 31%. Therefore, the lever-arm method is not recommended for use in its current form.

CLINICAL RELEVANCE

This study may help guide the development of a fast and simple method for determining EKAMs for individuals with knee osteoarthritis.

Instrumented knee joint implants: innovations and promising concepts

This article focuses on in vivo implementations of instrumented knee implants and recent prototypes with highly innovative potential. An in-depth analysis of the evolution of these systems was conducted, including three architectures developed by two research teams for in vivo operation that were implanted in 13 patients. The specifications of their various subsystems: sensor/transducers, power management, communication and processing/control units are presented, and their features are compared. These systems were designed to measure biomechanical quantities to further assist in rehabilitation and physical therapy, to access proper implant placement and joint function and to help predicting aseptic loosening. Five prototype systems that aim to improve their operation, as well as include new abilities, are also featured. They include technology to assist proper ligament tensioning and ensure self-powering. One can conclude that the concept of instrumented active knee implant seems the most promising trend for improving the outcomes of knee replacements.

Contribution of tibiofemoral joint contact to net loads at the knee in gait

Inverse dynamics analysis is commonly used to estimate the net loads at a joint during human motion. Most lower-limb models of movement represent the knee as a simple hinge joint when calculating muscle forces. This approach is limited because it neglects the contributions from tibiofemoral joint contact forces and may therefore lead to errors in estimated muscle forces. The aim of this study was to quantify the contributions of tibiofemoral joint contact loads to the net knee loads calculated from inverse dynamics for multiple subjects and multiple gait patterns. Tibiofemoral joint contact loads were measured in four subjects with instrumented implants as each subject walked at their preferred speed (normal gait) and performed prescribed gait modifications designed to treat medial knee osteoarthritis. Tibiofemoral contact loads contributed substantially to the net knee extension and knee adduction moments in normal gait with mean values of 16% and 54%, respectively. These findings suggest that knee-contact kinematics and loads should be included in lower-limb models of movement for more accurate determination of muscle forces. The results of this study may be used to guide the development of more realistic lower-limb models that account for the effects of tibiofemoral joint contact at the knee.

CONTRIBUTION OF MUSCLE TENSION FORCE TO MEDIAL KNEE CONTACT FORCE AT FAST WALKING SPEED

Fast walking is considered as a factor that causes pain in patients suffering from knee disorders. This study examined the effect of walking speed on the medial knee contact force and identified contributions to the muscle tension on the medial knee contact force during fast walking using musculoskeletal simulation analysis. The muscle contribution to the medial knee contact force was calculated based on the joint angles and ground reaction force for the normal and fast walking experiments of seven subjects. The muscle force and joint reaction force were used to estimate the medial knee contact force. Results showed, in average, 70% increase in medial knee contact force at the first peak and 34% increase at the second peak with a fast walking speed, compared to when they walked at a normal walking speed. The remarkable increase in the first peak was mainly contributed by the increase in the quadriceps force resisting the external knee flexion moment. In contrast, the moderate increase of second peak was contributed by the increase in the gastrocnemius muscle force. These results suggest that the increase in medial knee contact force at fast walking speeds is caused by the increased muscle force.

A Subject-Specific Musculoskeletal Modeling Framework to Predict In Vivo Mechanics of Total Knee Arthroplasty

Musculoskeletal (MS) models should be able to integrate patient-specific MS architecture and undergo thorough validation prior to their introduction into clinical practice. We present a methodology to develop subject-specific models able to simultaneously predict muscle, ligament, and knee joint contact forces along with secondary knee kinematics. The MS architecture of a generic cadaver-based model was scaled using an advanced morphing technique to the subject-specific morphology of a patient implanted with an instrumented total knee arthroplasty (TKA) available in the fifth “grand challenge competition to predict in vivo knee loads” dataset. We implemented two separate knee models, one employing traditional hinge constraints, which was solved using an inverse dynamics technique, and another one using an 11-degree-of-freedom (DOF) representation of the tibiofemoral (TF) and patellofemoral (PF) joints, which was solved using a combined inverse dynamic and quasi-static analysis, called force-dependent kinematics (FDK). TF joint forces for one gait and one right-turn trial and secondary knee kinematics for one unloaded leg-swing trial were predicted and evaluated using experimental data available in the grand challenge dataset. Total compressive TF contact forces were predicted by both hinge and FDK knee models with a root-mean-square error (RMSE) and a coefficient of determination (R2) smaller than 0.3 body weight (BW) and equal to 0.9 in the gait trial simulation and smaller than 0.4 BW and larger than 0.8 in the right-turn trial simulation, respectively. Total, medial, and lateral TF joint contact force predictions were highly similar, regardless of the type of knee model used. Medial (respectively lateral) TF forces were over- (respectively, under-) predicted with a magnitude error of M < 0.2 (respectively > −0.4) in the gait trial, and under- (respectively, over-) predicted with a magnitude error of M > −0.4 (respectively < 0.3) in the right-turn trial. Secondary knee kinematics from the unloaded leg-swing trial were overall better approximated using the FDK model (average Sprague and Geers' combined error C = 0.06) than when using a hinged knee model (C = 0.34). The proposed modeling approach allows detailed subject-specific scaling and personalization and does not contain any nonphysiological parameters. This modeling framework has potential applications in aiding the clinical decision-making in orthopedics procedures and as a tool for virtual implant design.

Osteoarthritis year in review 2014: mechanics--basic and clinical studies in osteoarthritis

The purpose of this review was to highlight recent research in mechanics and osteoarthritis (OA) by summarizing results from selected studies spanning basic and clinical research methods. Databases were searched from January 2013 through to March 2014. Working in pairs, reviewers selected 67 studies categorized into four themes--mechanobiology, ambulatory mechanics, biomechanical interventions and mechanical risk factors. Novel developments in mechanobiology included the identification of cell signaling pathways that mediated cellular responses to loading of articular cartilage. Studies in ambulatory mechanics included an increased focus on instrumented knee implants and progress in computational models, both emphasizing the importance of muscular contributions to load. Several proposed biomechanical interventions (e.g., shoe insoles and knee braces) produced variable changes in external knee joint moments during walking, while meta-analysis of randomized clinical trials did not support the use of lateral wedge insoles for decreasing pain. Results from high quality randomized trials suggested diet with or without exercise decreased indicators of knee joint load during walking, whereas similar effects from exercise alone were not detected with the measures used. Data from longitudinal cohorts suggested mechanical alignment was a risk factor for incidence and progression of OA, with the mechanism involving damage to the meniscus. In combination, the basic and clinical studies highlight the importance of considering multiple contributors to joint loading that can evoke both protective and damaging responses. Although challenges clearly exist, future studies should strive to integrate basic and clinical research methods to gain a greater understanding of the interactions among mechanical factors in OA and to develop improved preventive and therapeutic strategies.

Effect of increased pushoff during gait on hip joint forces

Anterior acetabular labral tears and anterior hip pain may result from high anteriorly directed forces from the femur on the acetabulum. While providing more pushoff is known to decrease sagittal plane hip moments, it is unknown if this gait modification also decreases hip joint forces. The purpose of this study was to determine if increasing pushoff decreases hip joint forces. Nine healthy subjects walked on an instrumented force treadmill at 1.25 m/s under two walking conditions. For the natural condition, subjects were instructed to walk as they normally would. For the increased pushoff condition, subjects were instructed to "push more with your foot when you walk". We collected motion data of markers placed on the subjects' trunk and lower extremities to capture trunk and leg kinematics and ground reaction force data to determine joint moments. Data were processed in Visual3D to produce the inverse kinematics and model scaling files. In OpenSim, the generic gait model (Gait2392) was scaled to the subject, and hip joint forces were calculated for the femur on the acetabulum after computing the muscle activations necessary to reproduce the experimental data. The instruction to "push more with your foot when you walk" reduced the maximum hip flexion and extension moment compared to the natural condition. The average reduction in the hip joint forces were 12.5%, 3.2% and 9.6% in the anterior, superior and medial directions respectively and 2.3% for the net resultant force. Increasing pushoff may be an effective gait modification for people with anterior hip pain.

Knee adduction moment and medial knee contact force during gait in older people

External knee adduction moment has been studied as a surrogate for medial knee contact force. However, it is not known whether adduction moment is a rational measure for predicting medial knee contact force. The aim of this study was to investigate the correlation between knee adduction moment and medial knee contact force in older people, using musculo-skeletal simulation analysis. One hundred and twenty-two healthy older subjects participated in this study. Knee moment and medial knee contact force were calculated based on inverse dynamics analysis of normal walking. Muscle force and joint reaction force were used to determine the medial knee contact force during stance phase. The results showed that the maximum medial knee contact force was moderately correlated to the maximum knee adduction (r = 0.59) as well as the maximum extension moment (r = 0.60). The first peak of medial knee contact force had a significant strong correlation with the first peak of adduction moment and a moderate correlation with the maximum flexion moment. The second peak of medial knee contact force had a significant moderate correlation with both the second peak of adduction and the maximum extension moment. These results implied that the maximum adduction moment value could be used, to some extent, as a measure of the maximum medial knee contact force.

On your feet: protocol for a randomized controlled trial to compare the effects of pole walking and regular walking on physical and psychosocial health in older adults

Background

Physical activity is associated with better physical and mental health in older adults. Pole walking is a form of walking which may have additional health benefits in older adults, because of the addition of hand held poles, and consequent upper limb involvement. However, few studies have examined the potential additional effects of pole walking on physical and psychosocial health in older adults compared with walking. The aim of this study is to compare the effect of a pole walking program with the effects of a walking program, on physical and psychosocial wellbeing, in older adults in assisted living facilities.

Methods/Design

Sixty men and women from assisted living communities over 65 years will be recruited from senior retirement facilities and randomized into a group based, pole walking program, or walking program. The pole walking group will use the Exerstrider method of pole walking. Total duration of the programs is 12 weeks, with three sessions per week, building from 20 minute to 30 minute sessions.

The primary outcome is physical function, as measured by items from the Seniors Fitness Test and hand grip strength. Secondary outcomes include, physical activity levels, sedentary behaviour, joint pain, and quality of life. All outcomes will be assessed before and after the programs, using valid and reliable measures.

Discussion

The study will add to the evidence base for the effects of pole walking, compared with walking, on physical and psychosocial health and physical function, in healthy older adults. This will improve understanding about the feasibility of pole walking programs and its specific benefits in this population.

Trial registration

Australian New Zealand Clinical Trials Registry ACTRN12612001127897.

Partitioning of knee joint internal forces in gait is dictated by the knee adduction angle and not by the knee adduction moment

Medial knee osteoarthritis is a debilitating disease. Surgical and conservative interventions are performed to manage its progression via reduction of load on the medial compartment or equivalently its surrogate measure, the external adduction moment. However, some studies have questioned a correlation between the medial load and adduction moment. Using a musculoskeletal model of the lower extremity driven by kinematics-kinetics of asymptomatic subjects at gait midstance, we aim here to quantify the relative effects of changes in the knee adduction angle versus changes in the adduction moment on the joint response and medial/lateral load partitioning. The reference adduction rotation of 1.6° is altered by ±1.5° to 3.1° and 0.1° or the knee reference adduction moment of 17Nm is varied by ±50% to 25.5Nm and 8.5Nm. Quadriceps, hamstrings and tibiofemoral contact forces substantially increased as adduction angle dropped and diminished as it increased. The medial/lateral ratio of contact forces slightly altered by changes in the adduction moment but a larger adduction rotation hugely increased this ratio from 8.8 to a 90 while in contrast a smaller adduction rotation yielded a more uniform distribution. If the aim in an intervention is to diminish the medial contact force and medial/lateral load ratio, a drop of 1.5° in adduction angle is much more effective (causing respectively 12% and 80% decreases) than a reduction of 50% in the adduction moment (causing respectively 4% and 13% decreases). Substantial role of changes in adduction angle is due to the associated alterations in joint nonlinear passive resistance. These findings explain the poor correlation between knee adduction moment and tibiofemoral compartment loading during gait suggesting that the internal load partitioning is dictated by the joint adduction angle.

Knee adduction moment and medial contact force--facts about their correlation during gait

The external knee adduction moment is considered a surrogate measure for the medial tibiofemoral contact force and is commonly used to quantify the load reducing effect of orthopedic interventions. However, only limited and controversial data exist about the correlation between adduction moment and medial force. The objective of this study was to examine whether the adduction moment is indeed a strong predictor for the medial force by determining their correlation during gait. Instrumented knee implants with telemetric data transmission were used to measure tibiofemoral contact forces in nine subjects. Gait analyses were performed simultaneously to the joint load measurements. Skeletal kinematics, as well as the ground reaction forces and inertial parameters, were used as inputs in an inverse dynamics approach to calculate the external knee adduction moment. Linear regression analysis was used to analyze the correlation between adduction moment and medial force for the whole stance phase and separately for the early and late stance phase. Whereas only moderate correlations between adduction moment and medial force were observed throughout the whole stance phase (R(2) = 0.56) and during the late stance phase (R(2) = 0.51), a high correlation was observed at the early stance phase (R(2) = 0.76). Furthermore, the adduction moment was highly correlated to the medial force ratio throughout the whole stance phase (R(2) = 0.75). These results suggest that the adduction moment is a surrogate measure, well-suited to predicting the medial force ratio throughout the whole stance phase or medial force during the early stance phase. However, particularly during the late stance phase, moderate correlations and high inter-individual variations revealed that the predictive value of the adduction moment is limited. Further analyses are necessary to examine whether a combination of other kinematic, kinetic or neuromuscular factors may lead to a more reliable prediction of the force magnitude.

Evolving strategies in mechanobiology to more effectively treat damaged musculoskeletal tissues

In this paper, we had four primary objectives. (1) We reviewed a brief history of the Lissner award and the individual for whom it is named, H.R. Lissner. We examined the type (musculoskeletal, cardiovascular, and other) and scale (organism to molecular) of research performed by prior Lissner awardees using a hierarchical paradigm adopted at the 2007 Biomechanics Summit of the US National Committee on Biomechanics. (2) We compared the research conducted by the Lissner award winners working in the musculoskeletal (MS) field with the evolution of our MS research and showed similar trends in scale over the past 35 years. (3) We discussed our evolving mechanobiology strategies for treating musculoskeletal injuries by accounting for clinical, biomechanical, and biological considerations. These strategies included studies to determine the function of the anterior cruciate ligament and its graft replacements as well as novel methods to enhance soft tissue healing using tissue engineering, functional tissue engineering, and, more recently, fundamental tissue engineering approaches. (4) We concluded with thoughts about future directions, suggesting grand challenges still facing bioengineers as well as the immense opportunities for young investigators working in musculoskeletal research. Hopefully, these retrospective and prospective analyses will be useful as the ASME Bioengineering Division charts future research directions.