ArticleBioengineering (Basel, Switzerland)2026
Pareto-Based Diagnostics and Selection for Mechanics-Synergy Trade-Offs in Unmeasured Muscle Activation Reconstruction.
Article in Bioengineering (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundReconstructing 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.
methodsWe 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.
resultsVerification 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.
conclusionsThe proposed framework makes mechanics-synergy trade-offs explicit and provides structured diagnostics and selection guidance for sparse-measurement scenarios.
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