Lower Extremity Inverse Kinematics Results Differ Between Inertial Measurement Unit- and Marker-Derived Gait Data.

In-lab, marker-based gait analyses may not represent real-world gait. Real-world gait analyses may be feasible using inertial measurement units (IMUs) in combination with open-source data processing pipelines (OpenSense). Before using OpenSense to study real-world gait, we must determine whether these methods estimate joint kinematics similarly to traditional marker-based motion capture (MoCap) and differentiate groups with clinically different gait mechanics. Healthy young and older adults and older adults with knee osteoarthritis completed this study. We captured MoCap and IMU data during overground walking at 2 speeds. MoCap and IMU kinematics were computed with OpenSim workflows. We tested whether sagittal kinematics differed between MoCap and IMU, whether tools detected between-group differences similarly, and whether kinematics differed between tools by speed. MoCap showed more anterior pelvic tilt (0%-100% stride) and joint flexion than IMU (hip: 0%-38% and 61%-100% stride; knee: 0%-38%, 58%-89%, and 95%-99% stride; and ankle: 6%-99% stride). There were no significant tool-by-group interactions. We found significant tool-by-speed interactions for all angles. While MoCap- and IMU-derived kinematics differed, the lack of tool-by-group interactions suggests consistent tracking across clinical cohorts. Results of the current study suggest that IMU-derived kinematics with OpenSense may enable reliable evaluation of gait in real-world settings.

Patterns of asymmetry and energy cost generated from predictive simulations of hemiparetic gait.

Hemiparesis, defined as unilateral muscle weakness, often occurs in people post-stroke or people with cerebral palsy, however it is difficult to understand how this hemiparesis affects movement patterns as it often presents alongside a variety of other neuromuscular impairments. Predictive musculoskeletal modeling presents an opportunity to investigate how impairments affect gait performance assuming a particular cost function. Here, we use predictive simulation to quantify the spatiotemporal asymmetries and changes to metabolic cost that emerge when muscle strength is unilaterally reduced and how reducing spatiotemporal symmetry affects metabolic cost. We modified a 2-D musculoskeletal model by uniformly reducing the peak isometric muscle force unilaterally. We then solved optimal control simulations of walking across a range of speeds by minimizing the sum of the cubed muscle excitations. Lastly, we ran additional optimizations to test if reducing spatiotemporal asymmetry would result in an increase in metabolic cost. Our results showed that the magnitude and direction of effort-optimal spatiotemporal asymmetries depends on both the gait speed and level of weakness. Also, the optimal speed was 1.25 m/s for the symmetrical and 20% weakness models but slower (1.00 m/s) for the 40% and 60% weakness models, suggesting that hemiparesis can account for a portion of the slower gait speed seen in people with hemiparesis. Modifying the cost function to minimize spatiotemporal asymmetry resulted in small increases (~4%) in metabolic cost. Overall, our results indicate that spatiotemporal asymmetry may be optimal for people with hemiparesis. Additionally, the effect of speed and the level of weakness on spatiotemporal asymmetry may help explain the well-known heterogenous distribution of spatiotemporal asymmetries observed in the clinic. Future work could extend our results by testing the effects of other neuromuscular impairments on optimal gait strategies, and therefore build a more comprehensive understanding of the gait patterns observed in clinical populations.

Using Bayesian inference to estimate plausible muscle forces in musculoskeletal models.

BACKGROUND: Musculoskeletal modeling is currently a preferred method for estimating the muscle forces that underlie observed movements. However, these estimates are sensitive to a variety of assumptions and uncertainties, which creates difficulty when trying to interpret the muscle forces from musculoskeletal simulations. Here, we describe an approach that uses Bayesian inference to identify plausible ranges of muscle forces for a simple motion while representing uncertainty in the measurement of the motion and the objective function used to solve the muscle redundancy problem. METHODS: We generated a reference elbow flexion-extension motion and computed a set of reference forces that would produce the motion while minimizing muscle excitations cubed via OpenSim Moco. We then used a Markov Chain Monte Carlo (MCMC) algorithm to sample from a posterior probability distribution of muscle excitations that would result in the reference elbow motion. We constructed a prior over the excitation parameters which down-weighted regions of the parameter space with greater muscle excitations. We used muscle excitations to find the corresponding kinematics using OpenSim, where the error in position and velocity trajectories (likelihood function) was combined with the sum of the cubed muscle excitations integrated over time (prior function) to compute the posterior probability density. RESULTS: We evaluated the muscle forces that resulted from the set of excitations that were visited in the MCMC chain (seven parallel chains, 500,000 iterations per chain). The estimated muscle forces compared favorably with the reference forces generated with OpenSim Moco, while the elbow angle and velocity from MCMC matched closely with the reference (average RMSE for elbow angle = 2°; and angular velocity = 32°/s). However, our rank plot analyses and potential scale reduction statistics, which we used to evaluate convergence of the algorithm, indicated that the chains did not fully mix. CONCLUSIONS: While the results from this process are a promising step towards characterizing uncertainty in muscle force estimation, the computational time required to search the solution space with, and the lack of MCMC convergence indicates that further developments in MCMC algorithms are necessary for this process to become feasible for larger-scale models.

The effects of posture on the three-dimensional gait mechanics of human walking in comparison with walking in bipedal chimpanzees.

Humans walk with an upright posture on extended limbs during stance and with a double-peaked vertical ground reaction force. Our closest living relatives, chimpanzees, are facultative bipeds that walk with a crouched posture on flexed, abducted hind limbs and with a single-peaked vertical ground reaction force. Differences in human and bipedal chimpanzee three-dimensional (3D) kinematics have been well quantified, yet it is unclear what the independent effects of using a crouched posture are on 3D gait mechanics for humans, and how they compare with chimpanzees. Understanding the relationships between posture and gait mechanics, with known differences in morphology between species, can help researchers better interpret the effects of trait evolution on bipedal walking. We quantified pelvis and lower limb 3D kinematics and ground reaction forces as humans adopted a series of upright and crouched postures and compared them with data from bipedal chimpanzee walking. Human crouched-posture gait mechanics were more similar to that of bipedal chimpanzee gait than to normal human walking, especially in sagittal plane hip and knee angles. However, there were persistent differences between species, as humans walked with less transverse plane pelvis rotation, less hip abduction, and greater peak anterior-posterior ground reaction force in late stance than chimpanzees. Our results suggest that human crouched-posture walking reproduces only a small subset of the characteristics of 3D kinematics and ground reaction forces of chimpanzee walking, with the remaining differences likely due to the distinct musculoskeletal morphologies of humans and chimpanzees.

Comparison of measurement protocols to estimate preferred walking speed between sites.

BACKGROUND: Walking speed influences a variety of typical outcome measures in gait analysis. Many researchers use a participant's preferred walking speed (PWS) during gait analysis with a goal of trying to capture how a participant would typically walk. However, the best practices for estimating PWS and the impact of laboratory size and walk distance are still unclear. RESEARCH QUESTION: Is measured PWS consistent across different distances and between two laboratory sites? METHODS: Participants walked overground at a "comfortable speed" for six different conditions with either dynamic (4, 6, 10, and 400 m) or static (4 and 10 m) starts and stops at two different data collection sites. Repeated measures ANOVA with Bonferroni corrections were used to test for differences between conditions and sites. RESULTS: Participants walked significantly faster in the 4, 6, and 10 m dynamic conditions than in the 400 m condition. On average, participants walked slower in the static trials than the dynamic trials of the same distance. There was a significant interaction of lab and condition and so results were examined within each lab. Across both labs, we found that the 4 and 10 m dynamic conditions were not different than the 6 m dynamic condition at both sites, while other tests did not provide consistent results at both sites. SIGNIFICANCE: We recommend researchers use a 6 m distance with acceleration and deceleration zones to reliably test for PWS across different laboratories. Given some of the differences found between conditions that varied by site, we also emphasize the need to report the test environment and methods used to estimate PWS in all future studies so that the methods can be replicated between studies.

A model-based motion capture marker location refinement approach using inverse kinematics from dynamic trials.

Marker-based motion capture techniques are commonly used to measure human body kinematics. These techniques require an accurate mapping from physical marker position to model marker position. Traditional methods utilize a manual process to achieve marker positions that result in accurate tracking. In this work, we present an optimization algorithm for model marker placement to minimize marker tracking error during inverse kinematics analysis of dynamic human motion. The algorithm sequentially adjusts model marker locations in 3-D relative to the underlying rigid segment. Inverse kinematics is performed for a dynamic motion capture trial to calculate the tracking error each time a marker position is changed. The increase or decrease of the tracking error determines the search direction and number of increments for each marker coordinate. A final marker placement for the model is reached when the total search interval size for every coordinate falls below a user-defined threshold. Individual marker coordinates can be locked in place to prevent the algorithm from overcorrecting for data artifacts such as soft tissue artifact. This approach was used to refine model marker placements for eight able-bodied subjects performing walking trials at three stride frequencies. Across all subjects and stride frequencies, root mean square (RMS) tracking error decreased by 38.4% and RMS tracking error variance decreased by 53.7% on average. The resulting joint kinematics were in agreement with expected values from the literature. This approach results in realistic kinematics with marker tracking errors well below accepted thresholds while removing variance in the model-building procedure introduced by individual human tendencies.

Bilevel Optimization for Cost Function Determination in Dynamic Simulation of Human Gait.

Predictive simulation based on dynamic optimization using musculoskeletal models is a powerful approach for studying human gait. Predictive musculoskeletal simulation may be used for a variety of applications from designing assistive devices to testing theories of motor control. However, the underlying cost function for the predictive optimization is unknown and is generally assumed a priori. Alternatively, the underlying cost function can be determined from among a family of possible cost functions, representing an inverse optimal control problem that may be solved using a bilevel optimization approach. In this study, a nested evolutionary approach is proposed to solve the bilevel optimization problem. The lower level optimization is solved by a direct collocation method, and the upper level is solved by a genetic algorithm. We demonstrate our approach to solve different bilevel optimization problems, including finding the weights among three common performance criteria in the cost function for normal human walking. The proposed approach was found to be effective at solving the bilevel optimization problems. This approach should provide practical utility in designing assistive devices to aid mobility, and could yield insights about the control of human walking.

Systematic review and meta-analysis of gait mechanics in young and older adults.

BACKGROUND: Age-related gait changes may play a critical role in functional limitations of older adults. Despite sizable interest in determining how age alters walking mechanics, small sample sizes and varied outcome measures have precluded a comprehensive understanding of the impact of age on lower extremity joint kinematics and kinetics. OBJECTIVE: The aim of this study was to perform a systematic review and meta-analysis of the aging gait mechanics literature. METHODS: The overall standardized effect of age on walking mechanics was computed for 29 studies (200 standardized effects). To account for variation in reported outcome variables, analyses were carried out for comparisons between young and older adult results using all discrete kinematic or kinetic variables reported for the ankle, knee, or hip. Different variables reported for a given joint were then analyzed as separate categorical moderators. RESULTS: The overall standardized effect of age was large for ground reaction forces, moderate for ankle and small for knee and hip kinematics and ankle and hip kinetics. When the analysis was restricted to studies with similar or matched walking speed, the standardized effects of age remained similar except for hip power generation and knee kinematic variables. CONCLUSIONS: The results of this meta-analysis provide evidence to support moderate standardized effects, with and without consideration of walking speeds, for changes in lower extremity kinematics, joint moments and powers at the ankle, and ground reaction forces. The standardized effects of age for knee mechanics are less conclusive and would benefit from further research.