Marzieh M. Ardestani

Human lower extremity joint moment prediction: A wavelet neural network approach

Joint moment is one of the most important factors in human gait analysis. It can be calculated using multi body dynamics but might not be straight forward. This study had two main purposes; firstly, to develop a generic multi-dimensional wavelet neural network (WNN) as a real-time surrogate model to calculate lower extremity joint moments and compare with those determined by multi body dynamics approach, secondly, to compare the calculation accuracy of WNN with feed forward artificial neural network (FFANN) as a traditional intelligent predictive structure in biomechanics. To aim these purposes, data of four patients walked with three different conditions were obtained from the literature. A total of 10 inputs including eight electromyography (EMG) signals and two ground reaction force (GRF) components were determined as the most informative inputs for the WNN based on the mutual information technique. Prediction ability of the network was tested at two different levels of inter-subject generalization. The WNN predictions were validated against outputs from multi body dynamics method in terms of normalized root mean square error (NRMSE (%)) and cross correlation coefficient (@r). Results showed that WNN can predict joint moments to a high level of accuracy (NRMSE 0.94) compared to FFANN (NRMSE 0.89). A generic WNN could also calculate joint moments much faster and easier than multi body dynamics approach based on GRFs and EMG signals which released the necessity of motion capture. It is therefore indicated that the WNN can be a surrogate model for real-time gait biomechanics evaluation.

Prediction of in vivo joint mechanics of an artificial knee implant using rigid multi-body dynamics with elastic contacts

Lower extremity musculoskeletal computational models play an important role in predicting joint forces and muscle activation simultaneously and are valuable for investigating functional outcomes of the implants. However, current computational musculoskeletal models of total knee replacement rarely consider the bearing surface geometry of the implant. Therefore, these models lack detailed information about the contact loading and joint motion which are important factors for evaluating clinical performances. This study extended a rigid multi-body dynamics simulation of a lower extremity musculoskeletal model to incorporate an artificial knee joint, based upon a novel force-dependent kinematics method, and to characterize the in vivo joint contact mechanics during gait. The developed musculoskeletal total knee replacement model integrated the rigid skeleton multi-body dynamics and the flexible contact mechanics of the tibiofemoral and patellofemoral joints. The predicted contact forces and muscle activations are compared against those in vivo measurements obtained from a single patient with good agreements for the medial contact force (root-mean-square error = 215 N, ρ = 0.96) and lateral contact force (root-mean-square error = 179 N, ρ = 0.75). Moreover, the developed model also predicted the motion of the tibiofemoral joint in all degrees of freedom. This new model provides an important step toward the development of a realistic dynamic musculoskeletal total knee replacement model to predict in vivo knee joint motion and loading simultaneously. This could offer a better opportunity to establish a robust virtual modeling platform for future pre-clinical assessment of knee prosthesis designs, surgical procedures and post-operation rehabilitation.

From normal to fast walking: Impact of cadence and stride length on lower extremity joint moments

This study aimed to clarify the influence of various speeding strategies (i.e. adjustments of cadence and stride length) on external joint moments. This study investigated the gait of 52 healthy subjects who performed self-selected normal and fast speed walking trials in a motion analysis laboratory. Subjects were classified into three separate groups based on how they increased their speed from normal to fast walking: (i) subjects who increased their cadence, (ii) subjects who increased their stride length and (iii) subjects who simultaneously increased both stride length and cadence. Joint moments were calculated using inverse dynamics and then compared between normal and fast speed trials within and between three groups using spatial parameter mapping. Individuals who increased cadence, but not stride length, to walk faster did not experience a significant increase in the lower limb joint moments. Conversely, subjects who increased their stride length or both stride length and cadence, experienced a significant increase in all joint moments. Additionally, our findings revealed that increasing the stride length had a higher impact on joint moments in the sagittal plane than those in the frontal plane. However, both sagittal and frontal plane moments were still more responsive to the gait speed change than transverse plane moments. This study suggests that the role of speed in altering the joint moment patterns depends on the individuals speed-regulating strategy, i.e. an increase in cadence or stride length. Since the confounding effect of walking speed is a major consideration in human gait research, future studies may investigate whether stride length is the confounding variable of interest.

Gait modification and optimization using neural network-genetic algorithm approach: Application to knee rehabilitation

Gait modification strategies play an important role in the overall success of total knee arthroplasty. There are a number of studies based on multi-body dynamic (MBD) analysis that have minimized knee adduction moment to offload knee joint. Reducing the knee adduction moment, without consideration of the actual contact pressure, has its own limitations. Moreover, MBD-based framework that mainly relies on iterative trial-and-error analysis, is fairly time consuming. This study embedded a time-delay neural network (TDNN) in a genetic algorithm (GA) as a cost effective computational framework to minimize contact pressure. Multi-body dynamic and finite element analyses were performed to calculate gait kinematics/kinetics and the resultant contact pressure for a number of experimental gait trials. A TDNN was trained to learn the nonlinear relation between gait parameters (inputs) and contact pressures (output). The trained network was then served as a real-time cost function in a GA-based global optimization to calculate contact pressure associated with each potential gait pattern. Two optimization problems were solved: first, knee flexion angle was bounded within the normal patterns and second, knee flexion angle was allowed to be increased beyond the normal walking. Designed gait patterns were evaluated through multi-body dynamic and finite element analyses. The TDNN-GA resulted in realistic gait patterns, compared to literature, which could effectively reduce contact pressure at the medial tibiofemoral knee joint. The first optimized gait pattern reduced the knee contact pressure by up to 21% through modifying the adjacent joint kinematics whilst knee flexion was preserved within normal walking. The second optimized gait pattern achieved a more effective pressure reduction (25%) through a slight increase in the knee flexion at the cost of considerable increase in the ankle joint forces. The proposed approach is a cost-effective computational technique that can be used to design a variety of rehabilitation strategies for different joint replacement with multiple objectives.

Contribution of geometric design parameters to knee implant performance: Conflicting impact of conformity on kinematics and contact mechanics

BACKGROUND Articular geometry of knee implant has a competing impact on kinematics and contact mechanics of total knee arthroplasty (TKA) such that geometry with lower contact pressure will impose more constraints on knee kinematics. The geometric parameters that may cause this competing effect have not been well understood. This study aimed to quantify the underlying relationships between implant geometry as input and its performance metrics as output. METHODS Parametric dimensions of a fixed-bearing cruciate retaining implant were randomized to generate a number of perturbed implant geometries. Performance metrics (i.e., maximum contact pressure, anterior-posterior range of motion [A-P ROM] and internal-external range of motion [I-E ROM]) of each randomized design were calculated using finite element analysis. The relative contributions of individual geometric variables to the performance metrics were then determined in terms of sensitivity indices (SI). RESULTS The femoral and tibial distal or posterior radii and femoral frontal radius are the key parameters. In the sagittal plane, distal curvature of the femoral and tibial influenced both contact pressure, i.e., SI=0.57; SI=0.65, and A-P ROM, i.e., SI=0.58; SI=0.6, respectively. However, posterior curvature of the femoral and tibial implants had a smaller impact on the contact pressure, i.e., SI=0.31; SI=0.23 and a higher impact on the I-E ROM, i.e., SI=0.72; SI=0.58. It is noteworthy that in the frontal plane, frontal radius of the femoral implant impacted both contact pressure (SI=0.38) and I-E ROM (SI=0.35). CONCLUSION Findings of this study highlighted how changes in the conformity of the femoral and tibial can impact the performance metrics.

How human gait responds to muscle impairment in total knee arthroplasty patients: Muscular compensations and articular perturbations

Post-surgical muscle weakness is prevalent among patients who undergo total knee arthroplasty (TKA). We conducted a probabilistic multi-body dynamics (MBD) to determine whether and to what extent habitual gait patterns of TKA patients may accommodate strength deficits in lower extremity muscles. We analyzed muscular and articular compensations in response to various muscle impairments, and the minimum muscle strength requirements needed to preserve TKA gait patterns in its habitual status. Muscle weakness was simulated by reducing the strength parameter of muscle models in MBD analysis. Using impaired models, muscle and joint forces were calculated and compared versus those from baseline gait i.e. TKA habitual gait before simulating muscle weakness. Comparisons were conducted using a relatively new statistical approach for the evaluation of gait waveforms, i.e. Spatial Parameter Mapping (SPM). Principal component analysis was then conducted on the MBD results to quantify the sensitivity of every joint force component to individual muscle impairment. The results of this study contain clinically important, although preliminary, suggestions. Our findings suggested that: (1) hip flexor and ankle plantar flexor muscles compensated for hip extensor weakness; (2) hip extensor, hip adductor and ankle plantar flexor muscles compensated for hip flexor weakness; (3) hip and knee flexor muscles responded to hip abductor weakness; (4) knee flexor and hip abductor balanced hip adductor impairment; and (5) knee extensor and knee flexor weakness were compensated by hip extensor and hip flexor muscles. Future clinical studies are required to validate the results of this computational study.

Prediction of Polyethylene Wear Rates from Gait Biomechanics and Implant Positioning in Total Hip Replacement

BackgroundPatient-specific gait and surgical variables are known to play an important role in wear of total hip replacements (THR). However a rigorous model, capable of predicting wear rate based on a comprehensive set of subject-specific gait and component-positioning variables, has to our knowledge, not been reported.Questions/purpose(1) Are there any differences between patients with high, moderate, and low wear rate in terms of gait and/or positioning variables? (2) Can we design a model to predict the wear rate based on gait and positioning variables? (3) Which group of wear factors (gait or positioning) contributes more to the wear rate?Patients and MethodsData on patients undergoing primary unilateral THR who performed a postoperative gait test were screened for inclusion. We included patients with a 28-mm metal head and a hip cup made of noncrosslinked polyethylene (GUR 415 and 1050) from a single manufacturer (Zimmer, Inc). To calculate wear rates from radiographs, inclusion called for patients with a series of standing radiographs taken more than 1 year after surgery. Further, exclusion criteria were established to obtain reasonably reliable and homogeneous wear readings. Seventy-three (83% of included) patients met all criteria, and the final dataset consisted of 43 males and 30 females, 69 ± 10 years old, with a BMI of 27.3 ± 4.7 kg/m2. Wear rates of these patients were determined based on the relative displacement of the femoral head with regard to the cup using a validated computer-assisted X-ray wear-analysis suite. Three groups with low (< 0.1 mm/year), moderate (0.1 to 0.2 mm/year), and high (> 0.2 mm/year) wear were established. Wear prediction followed a two-step process: (1) linear discriminant analysis to estimate the level of wear (low, moderate, or high), and (2) multiple linear and nonlinear regression modeling to predict the exact wear rate from gait and implant-positioning variables for each level of wear.ResultsThere were no group differences for positioning and gait suggesting that wear differences are caused by a combination of wear factors rather than single variables. The linear discriminant analysis model correctly predicted the level of wear in 80% of patients with low wear, 87% of subjects with moderate wear, and 73% of subjects with high wear based on a combination of gait and positioning variables. For every wear level, multiple linear and nonlinear regression showed strong associations between gait biomechanics, implant positioning, and wear rate, with the nonlinear model having a higher prediction accuracy. Flexion-extension ROM and hip moments in the sagittal and transverse planes explained 42% to 60% of wear rate while positioning factors, (such as cup medialization and cup inclination angle) explained only 10% to 33%.ConclusionPatient-specific wear rates are associated with patients’ gait patterns. Gait pattern has a greater influence on wear than component positioning for traditional metal-on-polyethylene bearings.Clinical RelevanceThe consideration of individual gait bears potential to further reduce implant wear in THR. In the future, a predictive wear model may identify individual, modifiable wear factors for modern materials.