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

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

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 individual's 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

A neural network approach for determining gait modifications to reduce the contact force in knee joint implant

There is a growing interest in non-surgical gait rehabilitation treatments to reduce the loading in the knee joint. In particular, synergetic kinematic changes required for joint offloading should be determined individually for each subject. Previous studies for gait rehabilitation designs are typically relied on a “trial-and-error” approach, using multi-body dynamic (MBD) analysis. However MBD is fairly time demanding which prevents it to be used iteratively for each subject. This study employed an artificial neural network to develop a cost-effective computational framework for designing gait rehabilitation patterns. A feed forward artificial neural network (FFANN) was trained based on a number of experimental gait trials obtained from literature. The trained network was then hired to calculate the appropriate kinematic waveforms (output) needed to achieve desired knee joint loading patterns (input). An auxiliary neural network was also developed to update the ground reaction force and moment profiles with respect to the predicted kinematic waveforms. The feasibility and efficiency of the predicted kinematic patterns were then evaluated through MBD analysis. Resuls showed that FFANN-based predicted kinematics could effectively decrease the total knee joint reaction forces. Peak values of the resultant knee joint forces, with respect to the bodyweight (BW), were reduced by 20% BW and 25% BW in the midstance and the terminal stance phases. Impulse values of the knee joint loading patterns were also decreased by 17% BW*s and 24%BW*s in the corresponding phases. The FFANN-based framework suggested a cost-effective forward solution which directly calculated the kinematic variations needed to implement a given desired knee joint loading pattern. It is therefore expected that this approach provides potential advantages and further insights into knee rehabilitation designs

The Application of NCaRBS to the Trendelenburg Test and Total Hip Arthroplasty Outcome

This paper compares the frontal plane hip func- tion of subject’s known to have had hip arthroplasty via either the lateral (LA) or posterior (PA) surgical approaches and a group of subjects associated with no pathology (NP). This is investigated through the Trendelenburg test using 3D motion analysis and classification. Here, a recent develop- ment on the Classification and Ranking Belief Simplex (CaRBS) technique, able to undertake n-state classification, so termed NCaRBS is employed. The relationship between post-operative hip function measured during a Trendelen- burg Test using three patient characteristics (pelvic obliquity, frontal plane hip moment and frontal plane hip power) of LA, PA and NP subjects are modelled together. Using these characteristics, the classification accuracy was 93.75% for NP, 57.14% for LA, 38.46% for PA. There was a clear distinction between NP and post-surgical function. 3/6 LA subjects and 6/8 PA subjects were misclassified as having NP function, implying that greater function is restored following the PA to surgery. NCaRBS achieved a higher accuracy (65.116%) than through a linear discriminant analysis (48.837%). A Neural Network with two-nodes achieved the same accuracy (65.116%) and as expected was further improved with three-nodes (69.767%). A valuable benefit to the employment of the NCaRBS technique is the graphical exposition of the contribution of patient characteristics to the classification analysis

25th Annual Computational Neuroscience Meeting: CNS-2016

Abstracts of the 25th Annual Computational Neuroscience Meeting: CNS-2016 Seogwipo City, Jeju-do, South Korea. 2–7 July 201

Sensitivity analysis of human lower extremity joint moments due to changes in joint kinematics

Despite the widespread applications of human gait analysis, causal interactions between joint kinematics and joint moments have not been well documented. Typical gait studies are often limited to pure multi-body dynamics analysis of a few subjects which do not reveal the relative contributions of joint kinematics to joint moments. This study presented a computational approach to evaluate the sensitivity of joint moments due to variations of joint kinematics. A large data set of probabilistic joint kinematics and associated ground reaction forces were generated based on experimental data from literature. Multi-body dynamics analysis was then used to calculate joint moments with respect to the probabilistic gait cycles. Employing the principal component analysis (PCA), the relative contributions of individual joint kinematics to joint moments were computed in terms of sensitivity indices (SI). Results highlighted high sensitivity of (1) hip abduction moment due to changes in pelvis rotation (SI = 0.38) and hip abduction (SI = 0.4), (2) hip flexion moment due to changes in hip flexion (SI = 0.35) and knee flexion (SI = 0.26), (3) hip rotation moment due to changes in pelvis obliquity (SI = 0.28) and hip rotation (SI = 0.4), (4) knee adduction moment due to changes in pelvis rotation (SI = 0.35), hip abduction (SI = 0.32) and knee flexion (SI = 0.34), (5) knee flexion moment due to changes in pelvis rotation (SI = 0.29), hip flexion (SI = 0.28) and knee flexion (SI = 0.31), and (6) knee rotation moment due to changes in hip abduction (SI = 0.32), hip flexion and knee flexion (SI = 0.31). Highlighting the “cause-and-effect” relationships between joint kinematics and the resultant joint moments provides a fundamental understanding of human gait and can lead to design and optimization of current gait rehabilitation treatments