Biology subjects

Finley, J.

Publications and source records attributed to Finley, J..

6 recordsLinked to original sources

bioRxiv2026

Estimating Muscle Parameters via Hierarchical Bayesian Inference

Musculoskeletal models are widely used to relate muscle mechanics to movement patterns in biomechanics. Accurate estimation of muscle parameters is essential for building individualized models, yet most rely on generic parameters derived from cadaveric data that do not reflect subject-specific properties critical to force generation. Here, we introduce a hierarchical Bayesian framework that leverages surface electromyography (EMG) and torque data from isometric elbow tasks to estimate subject-specific muscle parameters, overcoming limitations of generic parameter sets. This approach accounts for both inter-individual variability and uncertainty in measurement and model structure. The model infers six key parameters per subject, including flexor and extensor muscle strength, tendon slack length, moment arm geometry, and nonlinear EMG-to-activation relationships. We estimated model parameters for 14 young, healthy adults performing isometric elbow flexion and extension at multiple joint angles and torque levels. The six-parameter hierarchical-Bayesian musculoskeletal model accurately reproduced measured net elbow torque (R{superscript 2} = 0.96) and outperformed simpler configurations. Muscle strength parameters varied substantially across individuals, from approximately 1.0 to 3.5. On average, participants exhibited about twice those of the OpenSim 26 generic model. In contrast, tendon slack length estimates varied minimally across subjects. Bilateral testing revealed moderate correlations between left- and right-arm parameters, supporting the models ability to capture subject-specific anatomical features. Cross-validation confirmed robust predictive performance, and convergence diagnostics indicated reliable sampling. Compared to traditional EMG-driven or imaging-based personalization methods, our approach quantifies uncertainty, enables partial pooling across subjects, and avoids reliance on invasive or time-intensive measurements. The framework is extensible to dynamic tasks and adaptable to clinical populations, including individuals post-stroke. These results demonstrate that hierarchical Bayesian inference can robustly personalize musculoskeletal models and advance our understanding of biomechanics.

bioengineering↗

bioRxiv2023

Multi-site identification and generalization of clusters of walking impairment in individuals with chronic stroke

BackgroundWalking patterns in stroke survivors are highly heterogeneous, which poses a challenge in systematizing treatment prescriptions for walking rehabilitation interventions. ObjectiveWe used bilateral spatiotemporal and force data during walking to create a multi-site research sample to: 1) identify clusters of walking behaviors in people post-stroke and neurotypical controls, and 2) determine the generalizability of these walking clusters across different research sites. We hypothesized that participants post-stroke will have different walking impairments resulting in different clusters of walking behaviors, which are also different from control participants. MethodsWe gathered data from 81 post-stroke participants across four research sites and collected data from 31 control participants. Using sparse K-means clustering, we identified walking clusters based on 17 spatiotemporal and force variables. We analyzed the biomechanical features within each cluster to characterize cluster-specific walking behaviors. We also assessed the generalizability of the clusters using a leave-one-out approach. ResultsWe identified four stroke clusters: a fast and asymmetric cluster, a moderate speed and asymmetric cluster, a slow cluster with frontal plane force asymmetries, and a slow and symmetric cluster. We also identified a moderate speed and symmetric gait cluster composed of controls and participants post-stroke. The moderate speed and asymmetric stroke cluster did not generalize across sites. ConclusionsAlthough post-stroke walking patterns are heterogenous, these patterns can be systematically classified into distinct clusters based on spatiotemporal and force data. Future interventions could target the key features that characterize each cluster to increase the efficacy of interventions to improve mobility in people post-stroke.

neuroscience↗

bioRxiv2022

Gait speed and individual characteristics can be used to predict specific gait metric magnitudes in neurotypical adults.

BackgroundGait biofeedback is commonly used to reduce gait dysfunction in a variety of clinical conditions. In these studies, participants alter their walking to reach the desired magnitude of a specific gait parameter (the biofeedback target) with each step. Biofeedback of parameters such as anterior ground reaction force and step length have been well-studied. Yet, there is no standardized methodology to set the target magnitude of these parameters. Here we present an approach to predict the anterior ground reaction force and step length of neurotypical adults walking at different speeds as a potential method for personalized gait biofeedback. Research questionTo determine if anterior ground reaction force and step lengths achieved during neurotypical walking could be predicted using gait speed and participants demographic and anthropomorphic characteristics. MethodsWe analyzed kinetic and kinematic data from 51 neurotypical adults who walked on a treadmill at up to eight speeds. We calculated the average peak anterior ground reaction force and step length of the right lower extremity at each speed. We used linear mixed-effects models to evaluate the effect of speed, leg length, mass, and age on anterior ground reaction force and step length. We fit the model to data from 37 participants and validated predictions from the final models on an independent dataset from 23 participants. ResultsFinal prediction models for anterior ground reaction force and step length both included speed, speed squared, age, mass, and leg length. The models both showed strong agreement between predicted and actual values on an independent dataset. SignificanceAnterior ground reaction force and step length for neurotypical adults can be predicted given an individuals gait speed, age, leg length, and mass. This may provide a standardized method to personalize targets for individuals with gait dysfunction in future studies of gait biofeedback.

bioinformatics↗

bioRxiv2021

Simulated hemiparesis increases optimal spatiotemporal gait asymmetry but not metabolic cost

Several neuromuscular impairments, such as weakness (hemiparesis), occur after an individual has a stroke, and these impairments primarily affect one side of the body more than the other. Predictive musculoskeletal modeling presents an opportunity to investigate how a specific impairment affects gait performance post-stroke. Therefore, our aim was to use to predictive simulation to quantify the spatiotemporal asymmetries and changes to metabolic cost that emerge when muscle strength is unilaterally reduced. We also determined how forced spatiotemporal symmetry affects metabolic cost. We modified a 2-D musculoskeletal model by uniformly reducing the peak isometric muscle force in all left-limb muscles. We then solved optimal control simulations of walking across a range of speeds by minimizing the sum of the cubed muscle excitations across all muscles. 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 metabolic cost of transport 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 post-stroke. Adding spatiotemporal asymmetry to the cost function resulted in small increases (~4%) in metabolic cost. Overall, our results indicate that spatiotemporal asymmetry may be optimal for people post-stroke, who have asymmetrical neuromuscular impairments. Additionally, the effect of speed and level of weakness on spatiotemporal asymmetry may 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 impairments on optimal gait strategies, and therefore build a more comprehensive understanding of the gait patterns in people post-stroke. Author SummaryA stroke causes damage to the brain. This typically results in several changes to the nervous and muscular (neuromuscular) system that change how people post-stroke tend to walk and perform other tasks. Individuals post-stroke tend to walk with an asymmetrical motion and expend more energy while walking than other age-matched individuals. We still do not understand how each specific change to the neuromuscular system is linked with changes in walking patterns, in part because it is difficult to test one individual change at a time in people. Instead, we can use a mathematical model of the musculoskeletal system that represents the individual changes to the muscular system that occur in people post-stroke. In this study, we modeled how a common change in people post-stroke (muscle weakness) can impact walking patterns. We found that the level of weakness and the walking speed affect the asymmetrical walking patterns of our models, but do not change the total energy cost. Overall, our study is one step towards better understanding how neuromuscular changes in people post-stroke affects walking patterns. This knowledge could be applied to identify rehabilitation strategies that are most likely to improve walking in people post-stroke.

bioengineering↗

bioRxiv2020

Using asymmetry to your advantage: learning to acquire and accept external assistance during prolonged split-belt walking

People often adapt their coordination patterns during walking to reduce energy cost by using sources of external assistance in the environment. Adaptation to walking on a split-belt treadmill, where one belt moves faster than the other, provides an opportunity for people to acquire positive work from the treadmill to reduce metabolic cost by modifying where they step on the faster belt. Though we know what people should do to acquire this assistance, this strategy is not observed during typical adaptation studies. Here, by extending the duration of adaptation, we show that people continuously optimize energetic cost by adjusting foot placement to acquire positive work from the treadmill and reduce the work performed by the legs. These results demonstrate that learning to acquire and take advantage of assistance to reduce energetic cost is central in shaping adaptive locomotion, but this process occurs over timescales longer than those used in typical studies.

neuroscience↗

bioRxiv2019

Asymmetric gait patterns alter the reactive control of intersegmental coordination patterns during walking

Recovery from perturbations during walking is primarily mediated by reactive control strategies that coordinate multiple body segments to maintain balance. Balance control is often impaired in clinical populations who walk with spatiotemporally asymmetric gait, and, as a result, rehabilitation efforts often seek to reduce asymmetries in these populations. Previous work has demonstrated that the presence of spatiotemporal asymmetries during walking does not impair the control of whole-body dynamics during perturbation recovery. However, it remains to be seen how the neuromotor system adjusts intersegmental coordination patterns to maintain invariant whole-body dynamics. Here, we determined if the neuromotor system generates stereotypical coordination patterns irrespective of the level of asymmetry or if the neuromotor system allows for variance in intersegmental coordination patterns to stabilize whole-body dynamics. Nineteen healthy participants walked on a dual-belt treadmill at a range of step length asymmetries, and they responded to unpredictable, slip-like perturbations. We used principal component analysis of segmental angular momenta to characterize intersegmental coordination patterns before, during, and after imposed perturbations. We found that two principal components were sufficient to explain ~ 95% of the variance in segmental angular momentum during both steading walking and responses to perturbations. Our results also revealed that walking with asymmetric step lengths led to changes in intersegmental coordination patterns during the perturbation and during subsequent recovery steps without affecting whole-body angular momentum. These results suggest that the nervous system allows for variance in segment-level coordination patterns to maintain invariant control of whole-body angular momentum during walking. Future studies exploring how these segmental coordination patterns change in individuals with asymmetries that result from neuromotor impairments can provide further insight into how the healthy and impaired nervous system regulates dynamic balance during walking.

neuroscience↗