Neural networks estimate muscle force in dynamic conditions better than Hill-type muscle models

Athanasiadou ME, Daley MA, Koelewijn A (2025)

Publication Type: Journal article

Publication year: 2025

Journal

Journal of Experimental Biology Company of Biologists

Book Volume: 228

Journal Issue: 22

DOI: 10.1242/jeb.250268

Abstract

Hill-type muscle models are widely used, even though they do not accurately represent the relationship between activation and force in dynamic contractions. We explored the use of neural networks as an alternative approach to capture features of dynamic muscle function, without a priori assumptions about force-length-velocity relationships. We trained neural networks using an existing dataset of two guinea fowl muscles to estimate muscle force from activation, fascicle length and velocity. Training data were recorded using sonomicrometry, electromyography and a tendon buckle. First, we compared the neural networks with Hill-type muscle models, using the same data for network training and model optimization. Second, we trained neural networks on larger datasets, in a more realistic machine learning scenario. We found that neural networks generally yielded higher coefficients of determination and lower errors than Hill-type muscle models. Neural networks performed better when estimating forces on the muscle used for training, but on another bird, than on a different muscle of the same bird, likely due to inaccuracies in activation and force scaling. We extracted force-length and force-velocity relationships from the trained neural networks and found that both effects were underestimated and the relationships were not well replicated outside the training data distribution. We discuss suggested experimental designs and the challenge of collecting suitable training data. Given a suitable training dataset, neural networks could provide a useful alternative to Hill-type muscle models, particularly for modeling muscle dynamics in faster movements; however, scaling of the training data should be comparable between muscles and animals.

Authors with CRIS profile

Maria Eleni Athanasiadou Professur für Computational Movement Science Anne Koelewijn Professur für Computational Movement Science

Involved external institutions

University of California Irvine US United States (USA) (US)

How to cite

APA:

Athanasiadou, M.E., Daley, M.A., & Koelewijn, A. (2025). Neural networks estimate muscle force in dynamic conditions better than Hill-type muscle models. Journal of Experimental Biology, 228(22). https://doi.org/10.1242/jeb.250268

MLA:

Athanasiadou, Maria Eleni, Monica A. Daley, and Anne Koelewijn. "Neural networks estimate muscle force in dynamic conditions better than Hill-type muscle models." Journal of Experimental Biology 228.22 (2025).

BibTeX: Download