Computational Neuromechanics

Projects

Optimization principles 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.

Task-oriented identification of motor modules

Deficits in motor control, such as an inability to selectively coordinate muscles, frequently occur after
stroke and can impair both upper and lower extremity function. Electromyography can be used to capture motor modules, which represent patterns of muscle activity can be flexibly combined to produce a motor behavior. Changes in the composition of these modules correlates with gait deficits after stroke and
improvements during recovery. Here, we are developing a novel method of motor module extraction to illuminate stroke-related coordination changes free of confounding differences in dynamics during task execution. These task-oriented motor modules are capable of reconstructing both observed EMG patterns and limb kinetics, linking patterns of motor commands to their resulting kinetic outcomes.

Bayesian inference in musculoskeletal modeling

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. We are interested in developing an approach that uses Bayesian inference to identify plausible ranges of muscle forces while capturing the uncertainty in the measurement of the motion and the objective function used to solve the muscle redundancy problem.

Publications

1. Valero-Cuevas FJ, Finley JM, Orsborn A, Fung N, Hicks JL, Huang H, Reinkensmeyer D, Schweighofer N, Weber D, Steele KM. Transforming modeling in neurorehabilitation: Four threads for catalyzing progress. Journal of Neuroengineering and Rehabilitation. 21 (1).
2. Johnson RT, Bianco NA, and Finley JM. (2022). Patterns of asymmetry and energy cost generated from predictive simulations of hemiparetic gait. PLOS Computational Biology. 18 (9) e1010466.
3. Johnson RT, Lakeland D, Finley JM. (2022). Using Bayesian inference to estimate plausible muscle forces in musculoskeletal models. Journal of Neuroengineering and Rehabilitation. 19:34.
4. Rebula J, Schaal S, Finley JM, Righetti L. (2021). A robustness analysis of inverse optimal control of bipedal walking. IEEE Robotics and Automation Letters, 4 (4).
5. Nozari, P., & Finley, J. M. (2019). Development of a Platform to Evaluate Principles of Bipedal Locomotion Using Dynamical Movement Primitives. In 2019 9th International IEEE/EMBS Conference on Neural Engineering (NER) (pp. 1062-1065). IEEE.
6. A. Marjaninejad and J.M. Finley. (2016). A model-based exploration of the role of pattern generating circuits during locomotor adaptation. Conf Proc IEEE Eng Med Biol Soc 1: 21-24.
7. Finley JM, Perreault EJ, Dhaher YY. (2008). Stretch reflex coupling between the hip and knee: Implications for impaired gait following stroke. Experimental Brain Research, 131, 305-319.

Funding