Allison Kinney

Update on Grand Challenge Competition to Predict in Vivo Knee Loads

Validation is critical if clinicians are to use musculoskeletal models to optimize treatment of individual patients with a variety of musculoskeletal disorders. This paper provides an update on the annual Grand Challenge Competition to Predict in Vivo Knee Loads, a unique opportunity for direct validation of knee contact forces and indirect validation of knee muscle forces predicted by musculoskeletal models. Three competitions (2010, 2011, and 2012) have been held at the annual American Society of Mechanical Engineers Summer Bioengineering Conference, and two more competitions are planned for the 2013 and 2014 conferences. Each year of the competition, a comprehensive data set collected from a single subject implanted with a force-measuring knee replacement is released. Competitors predict medial and lateral knee contact forces for two gait trials without knowledge of the experimental knee contact force measurements. Predictions are evaluated by calculating root-mean-square (RMS) errors and R(2) values relative to the experimentally measured medial and lateral contact forces. For the first three years of the competition, competitors used a variety of methods to predict knee contact and muscle forces, including static and dynamic optimization, EMG-driven models, and parametric numerical models. Overall, errors in predicted contact forces were comparable across years, with average RMS errors for the four competition winners ranging from 229 N to 312 N for medial contact force and from 238 N to 326 N for lateral contact force. Competitors generally predicted variations in medial contact force (highest R(2 )= 0.91) better than variations in lateral contact force (highest R(2 )= 0.70). Thus, significant room for improvement exists in the remaining two competitions. The entire musculoskeletal modeling community is encouraged to use the competition data and models for their own model validation efforts.

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

The ability to predict patient-specific joint contact and muscle forces accurately could improve the treatment of walking-related disorders. Muscle synergy analysis, which decomposes a large number of muscle electromyographic (EMG) signals into a small number of synergy control signals, could reduce the dimensionality and thus redundancy of the muscle and contact force prediction process. This study investigated whether use of subject-specific synergy controls can improve optimization prediction of knee contact forces during walking. To generate the predictions, we performed mixed dynamic muscle force optimizations (i.e., inverse skeletal dynamics with forward muscle activation and contraction dynamics) using data collected from a subject implanted with a force-measuring knee replacement. Twelve optimization problems (three cases with four subcases each) that minimized the sum of squares of muscle excitations were formulated to investigate how synergy controls affect knee contact force predictions. The three cases were: (1) Calibrate+Match where muscle model parameter values were calibrated and experimental knee contact forces were simultaneously matched, (2) Precalibrate+Predict where experimental knee contact forces were predicted using precalibrated muscle model parameters values from the first case, and (3) Calibrate+Predict where muscle model parameter values were calibrated and experimental knee contact forces were simultaneously predicted, all while matching inverse dynamic loads at the hip, knee, and ankle. The four subcases used either 44 independent controls or five synergy controls with and without EMG shape tracking. For the Calibrate+Match case, all four subcases closely reproduced the measured medial and lateral knee contact forces (R2 ≥ 0.94, root-mean-square (RMS) error < 66 N), indicating sufficient model fidelity for contact force prediction. For the Precalibrate+Predict and Calibrate+Predict cases, synergy controls yielded better contact force predictions (0.61 < R2 < 0.90, 83 N < RMS error < 161 N) than did independent controls (-0.15 < R2 < 0.79, 124 N < RMS error < 343 N) for corresponding subcases. For independent controls, contact force predictions improved when precalibrated model parameter values or EMG shape tracking was used. For synergy controls, contact force predictions were relatively insensitive to how model parameter values were calibrated, while EMG shape tracking made lateral (but not medial) contact force predictions worse. For the subject and optimization cost function analyzed in this study, use of subject-specific synergy controls improved the accuracy of knee contact force predictions, especially for lateral contact force when EMG shape tracking was omitted, and reduced prediction sensitivity to uncertainties in muscle model parameter values.

Evaluation of Direct Collocation Optimal Control Problem Formulations for Solving the Muscle Redundancy Problem.

Estimation of muscle forces during motion involves solving an indeterminate problem (more unknown muscle forces than joint moment constraints), frequently via optimization methods. When the dynamics of muscle activation and contraction are modeled for consistency with muscle physiology, the resulting optimization problem is dynamic and challenging to solve. This study sought to identify a robust and computationally efficient formulation for solving these dynamic optimization problems using direct collocation optimal control methods. Four problem formulations were investigated for walking based on both a two and three dimensional model. Formulations differed in the use of either an explicit or implicit representation of contraction dynamics with either muscle length or tendon force as a state variable. The implicit representations introduced additional controls defined as the time derivatives of the states, allowing the nonlinear equations describing contraction dynamics to be imposed as algebraic path constraints, simplifying their evaluation. Problem formulation affected computational speed and robustness to the initial guess. The formulation that used explicit contraction dynamics with muscle length as a state failed to converge in most cases. In contrast, the two formulations that used implicit contraction dynamics converged to an optimal solution in all cases for all initial guesses, with tendon force as a state generally being the fastest. Future work should focus on comparing the present approach to other approaches for computing muscle forces. The present approach lacks some of the major limitations of established methods such as static optimization and computed muscle control while remaining computationally efficient.

Changes in In Vivo Knee Contact Forces through Gait Modification

Knee osteoarthritis (OA) commonly occurs in the medial compartment of the knee and has been linked to overloading of the medial articular cartilage. Gait modification represents a non‐invasive treatment strategy for reducing medial compartment knee force. The purpose of this study was to evaluate the effectiveness of a variety of gait modifications that were expected to alter medial contact force. A single subject implanted with a force‐measuring knee replacement walked using nine modified gait patterns, four of which involved different hiking pole configurations. Medial and lateral contact force at 25, 50, and 75% of stance phase, and the average value over all of stance phase (0–100%), were determined for each gait pattern. Changes in medial and lateral contact force values relative to the subjects normal gait pattern were determined by a Kruskal–Wallis test. Apart from early stance (25% of stance), medial contact force was most effectively reduced by walking with long hiking poles and wide pole placement, which significantly reduced medial and lateral contact force during stance phase by up to 34% (at 75% of stance) and 26% (at 50% of stance), respectively. Although this study is based on data from a single subject, the results provide important insight into changes in medial and lateral contact forces through gait modification. The results of this study suggest that an optimal configuration of bilateral hiking poles may significantly reduce both medial and lateral compartment knee forces in individuals with medial knee osteoarthritis.

Neuromusculoskeletal Model Calibration Significantly Affects Predicted Knee Contact Forces for Walking

Though walking impairments are prevalent in society, clinical treatments are often ineffective at restoring lost function. For this reason, researchers have begun to explore the use of patient-specific computational walking models to develop more effective treatments. However, the accuracy with which models can predict internal body forces in muscles and across joints depends on how well relevant model parameter values can be calibrated for the patient. This study investigated how knowledge of internal knee contact forces affects calibration of neuromusculoskeletal model parameter values and subsequent prediction of internal knee contact and leg muscle forces during walking. Model calibration was performed using a novel two-level optimization procedure applied to six normal walking trials from the Fourth Grand Challenge Competition to Predict In Vivo Knee Loads. The outer-level optimization adjusted time-invariant model parameter values to minimize passive muscle forces, reserve actuator moments, and model parameter value changes with (Approach A) and without (Approach B) tracking of experimental knee contact forces. Using the current guess for model parameter values but no knee contact force information, the inner-level optimization predicted time-varying muscle activations that were close to experimental muscle synergy patterns and consistent with the experimental inverse dynamic loads (both approaches). For all the six gait trials, Approach A predicted knee contact forces with high accuracy for both compartments (average correlation coefficient r = 0.99 and root mean square error (RMSE) = 52.6 N medial; average r = 0.95 and RMSE = 56.6 N lateral). In contrast, Approach B overpredicted contact force magnitude for both compartments (average RMSE = 323 N medial and 348 N lateral) and poorly matched contact force shape for the lateral compartment (average r = 0.90 medial and -0.10 lateral). Approach B had statistically higher lateral muscle forces and lateral optimal muscle fiber lengths but lower medial, central, and lateral normalized muscle fiber lengths compared to Approach A. These findings suggest that poorly calibrated model parameter values may be a major factor limiting the ability of neuromusculoskeletal models to predict knee contact and leg muscle forces accurately for walking.

How sensitive is the deltoid moment arm to humeral offset changes with reverse total shoulder arthroplasty

BACKGROUND Reverse total shoulder arthroplasty commonly treats cuff-deficient or osteoarthritic shoulders not amenable to rotator cuff repair. This study investigates deltoid moment arm sensitivity to variations in the joint center and humeral offset of 3 representative reverse total shoulder arthroplasty subjects. We hypothesized that a superior joint implant placement may exist, indicated by muscle moment arms, compared with the current actual surgical implant configuration. METHODS Moment arms for the anterior, lateral, and posterior aspects of the deltoid muscle were determined for 1521 perturbations of the humeral offset location away from the surgical placement in a subject-specific musculoskeletal model with motion defined by subject-specific in vivo abduction kinematics. The humeral offset was varied from its surgical position ±4 mm in the anterior/posterior direction, ±12 mm in the medial/lateral direction, and -10 to 14 mm in the superior/inferior direction. RESULTS The anterior deltoid moment arm varied in humeral offset and center of rotation up to 20 mm, primarily in the medial/lateral and superior/inferior directions. The lateral deltoid moment arm varied in humeral offset up to 20 mm, primarily in the medial/lateral and anterior/posterior directions. The posterior deltoid moment arm varied up to 15 mm, primarily in early abduction, and was most sensitive to humeral offset changes in the superior/inferior direction. DISCUSSION High variations in muscle moment arms were found for all 3 deltoid components, presenting an opportunity to dramatically change the deltoid moment arms through surgical placement of the reverse shoulder components and by varying the overall offset of the humerus. LEVEL OF EVIDENCE Basic Science Study; Computer Modeling.

Identification of key outcome measures when using the instrumented timed up and go and/or posturography for fall screening

The Timed Up and Go (TUG) has been commonly used for fall risk assessment. The instrumented Timed Up and Go (iTUG) adds wearable sensors to capture sub-movements and may be more sensitive. Posturography assessments have also been used for determining fall risk. This study used stepwise logistic regression models to identify key outcome measures for the iTUG and posturography protocols. The effectiveness of the models containing these measures in differentiating fallers from non-fallers were then compared for each: iTUG total time duration only, iTUG, posturography, and combined iTUG and posturography assessments. One hundred and fifty older adults participated in this study. The iTUG measures were calculated utilizing APDM Inc.s Mobility Lab software. Traditional and non-linear posturography measures were calculated from center of pressure during quiet-standing. The key outcome measures incorporated in the iTUG assessment model (sit-to-stand lean angle and height) resulted in a model sensitivity of 48.1% and max re-scaled R2 value of 0.19. This was a higher sensitivity, indicating better differentiation, compared to the model only including total time duration (outcome of the traditional TUG), which had a sensitivity of 18.2%. When the key outcome measures of the iTUG and the posturography assessments were combined into a single model, the sensitivity was approximately the same as the iTUG model alone. Overall the findings of this study support that the iTUG demonstrates greater sensitivity than the total time duration, but that carrying out both iTUG and posturography does not greatly improve sensitivity when used as a fall risk screening tool.

Muscle Synergy Constraints Improve Prediction of Knee Contact Force during Gait

Knowledge of patient-specific muscle and joint contact forces during activities of daily living could improve the treatment of movement-related disorders (e.g., osteoarthritis, stroke, cerebral palsy, Parkinson’s disease). Unfortunately, it is currently impossible to measure these quantities directly under common clinical conditions, and calculation of these quantities using computer models is limited by the redundant nature of human neural control (i.e., more muscles than theoretically necessary to actuate the available degrees of freedom in the skeleton). Walking is a particularly important task to understand, since loss of mobility is associated with increased morbidity and decreased quality of life [1]. Though numerous musculoskeletal computer modeling studies have used optimization methods to resolve the neural control redundancy problem, these estimates remain unvalidated due to the lack of internal force measurements that can be used for validation purposes.Copyright

Effects of Body Weight Modification on Internal Knee Contact Forces During Gait

Obesity is commonly considered a risk factor for the development of knee osteoarthritis [1]. Previous studies have shown that reductions in body weight correspond to reductions in total knee joint compressive forces (as calculated by inverse dynamics) [2–4]. A recent study showed that external knee load measurements are not strong predictors of internal knee contact forces [5]. Therefore, direct measurement of knee contact force is important for understanding how body weight changes impact knee joint loading. Force-measuring knee implants can directly measure internal knee contact forces [6].© 2013 ASME

Changes in Medial Knee Contact Force Through Gait Modification

The development of medial knee osteoarthritis (OA) has been attributed to overloading of the medial compartment articular cartilage [1]. Therefore, treatment strategies are often focused on reducing medial compartment loads. Gait modification represents a non-invasive method for achieving this goal. Previous studies have shown that a variety of gait modifications (e.g., toeing out, increased medial-lateral trunk sway, walking with medialized knees (i.e., medial thrust gait)) are effective in reducing the external knee adduction moment [e.g., 2–4]. Although the external knee adduction moment is often used as a surrogate measure of medial compartment force, a recent study showed that reductions in the external knee adduction moment do not guarantee reductions in medial compartment force [5]. Therefore, direct measurements of changes in medial contact force are important for determining the effectiveness of gait modifications.Copyright