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Updated: Mar 29, 2026
Evaluating Postural Control and Lower-extremity Muscle Activation in Individuals with Chronic Ankle Instability
Published on: September 18, 2020
Pareto-Based Diagnostics and Selection for Mechanics-Synergy Trade-Offs in Unmeasured Muscle Activation
Po-Hsien Jiang1, Kuei-Yuan Chan1
1Department of Mechanical Engineering, National Taiwan University, Taipei 10617, Taiwan.
Background:
Reconstructing full muscle activation trajectories from sparse measurements is underdetermined: many activation patterns can explain similar joint moments, and purely mechanical inverse formulations can yield non-physiological solutions.
Methods:
We propose a synergy-informed, physics-constrained framework to reconstruct unmeasured muscle activations when only a subset of muscles is observed. A synergy reconstruction prior (SynRc) is obtained by identifying a synergy basis from proxy activations via non-negative matrix factorization (NMF) and estimating time-varying synergy excitations from measured channels. Unmeasured activations are then solved via bound-constrained multi-objective optimization that jointly minimizes (i) normalized joint-moment error between OpenSim forward-computed moments and inverse-dynamics moments and (ii) deviation from the SynRc prior, with an optional smoothness refinement stage.
Results:
Verification on synthetic OpenSim Arm26 (2-DOF) cases with known ground truth shows that J1-dominant selections from the stage-I Pareto set reduce normalized joint-moment error from 0.154 (SynRc-only) to ≈0.138, at the cost of larger deviation from the synergy prior. These Pareto diagnostics expose identifiability and selection sensitivity under sparse measurements when ground truth is unavailable.
Conclusions:
The proposed framework makes mechanics-synergy trade-offs explicit and provides structured diagnostics and selection guidance for sparse-measurement scenarios.
