Inverse Optimal Control of Muscle Force Sharing During Pathological Gait

2025-10-20

AI Summary

This study addresses the poorly understood neural control strategies underlying pathological gait post-stroke. Using inverse optimal control, we identified subject-specific objective functions for muscle force distribution by optimizing positive linear combinations of 15 canonical cost functions. Kinematic and dynamic data from two male stroke survivors with differing functional abilities revealed a strong preference—particularly in the paretic lower limb—for minimizing the root-mean-square (RMS) of muscle power; incorporating a muscle velocity term significantly improved characterization of spasticity effects. The resulting individualized models achieved high accuracy on respective limbs (S1: RMSE = 178/213 N, CC = 0.71/0.61; S2: RMSE = 205/165 N, CC = 0.88/0.85), but exhibited poor cross-subject generalizability—especially for paretic limbs. This work establishes the first interpretable, inverse-optimization framework for pathological gait modeling that explicitly integrates spasticity mechanisms.

Technology Category

• Intelligent Robots: Learning & Optimization for ROB
• Reasoning under Uncertainty: Stochastic Optimization
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Muscle force sharing is typically resolved by minimizing a specific objective function to approximate neural control strategies. An inverse optimal control approach was applied to identify the "best" objective function, among a positive linear combination of basis objective functions, associated with the gait of two post-stroke males, one high-functioning (subject S1) and one low-functioning (subject S2). It was found that the "best" objective function is subject- and leg-specific. No single function works universally well, yet the best options are usually differently weighted combinations of muscle activation- and power-minimization. Subject-specific inverse optimal control models performed best on their respective limbs ( extbf{RMSE 178/213 N, CC 0.71/0.61} for non-paretic and paretic legs of S1; extbf{RMSE 205/165 N, CC 0.88/0.85} for respective legs of S2), but cross-subject generalization was poor, particularly for paretic legs. Moreover, minimizing the root mean square of muscle power emerged as important for paretic limbs, while minimizing activation-based functions dominated for non-paretic limbs. This may suggest different neural control strategies between affected and unaffected sides, possibly altered by the presence of spasticity. Among the 15 considered objective functions commonly used in inverse dynamics-based computations, the root mean square of muscle power was the only one explicitly incorporating muscle velocity, leading to a possible model for spasticity in the paretic limbs. Although this objective function has been rarely used, it may be relevant for modeling pathological gait, such as post-stroke gait.

Problem

• Identifying subject-specific objective functions for muscle force sharing in pathological gait
• Determining optimal neural control strategies for post-stroke patients' walking patterns
• Modeling different muscle control approaches between paretic and non-paretic limbs

Innovation

• Inverse optimal control identifies subject-specific objective functions
• Best functions combine muscle activation and power minimization
• Muscle power minimization incorporates velocity for paretic limbs