Back to search

Article

Estimating Muscle Parameters via Hierarchical Bayesian Neuromechanics

2026-07-09

Abstract excerpt

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 elect...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work
54131f2a-9754-508f-9f58-0a54b66ddc00
DOI
10.64898/2026.07.08.737322
Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest
Interactive article relationship graphSelect a related publication card to move it into the centre and load its closest explainable connections. Solid lines are source-backed structured connections. Dashed lines are semantic discovery signals and are not scientific evidence.
Estimating Muscle Parameters via Hierarchical Bayesian NeuromechanicsDOI 10.64898/2026.07.08.737322
Select a neighboring publication to make it the new centre.