# A Transfer Learning Framework for Creating Subject-Specific Musculoskeletal Models of the Hand

> **NIH NIH R21** · UNIVERSITY OF FLORIDA · 2020 · $560,939

## Abstract

PROJECT SUMMARY
Restoring hand function remains an elusive goal for many clinical conditions, including stroke, osteoarthritis,
tetraplegia, amputation, and traumatic injury. The hand’s anatomical complexity makes restoring hand function
particularly challenging because altering any one parameter in the hand can have cascading effects that are
difficult to predict, but essential to control. In this proposal, as a critical step toward informing personalized
treatments for the hand, we will study how subject-specific differences influence hand function. Completion of
this proposal will rely on collection of three datasets that are designed to provide varying levels of biomechanical
detail and require varying levels of effort to collect. Briefly, these datasets include (1) a simulation dataset
containing 500,000 simulations fully describing all musculoskeletal parameters involved in hand force production,
(2) a dense, biomechanical datasets that describes the kinematics, kinetics, and muscle activity required for
hand force production in 30 adults, and (3) a sparse, clinically-inspired dataset that describes demographics,
anthropometrics, and clinical metrics of hand function in 1000 adults. In Aim 1, we will leverage the first two
datasets to design a data-driven analysis framework that identifies the most important biomechanical
parameter(s) and maps how those parameters influence hand force production. Completion of this aim will
elucidate the biomechanical mechanisms that modulate hand force production and evaluate the ability to use
simulation data, instead of experimental data, to identify these mechanisms. In Aim 2, we will leverage all three
datasets to create a transfer learning framework capable of efficiently and accurately predicting subject-specific
muscle force-generating parameters from easy to collect clinical data. We specifically focus on muscle force-
generating parameters because these parameters remain challenging to quickly and accurately estimate, are
known to vary across the population, and are highly related to functional metrics like strength. Completion of this
aim will provide a new approach for rapidly estimating subject-specific musculoskeletal parameters, thereby
enabling efficient creation of subject-specific models and potentially catalyzing use of such models in a clinical
setting. Overall, the results from this study could enhance our ability to provide personalized diagnoses and
prognoses for individuals suffering from hand impairments.

## Key facts

- **NIH application ID:** 10040078
- **Project number:** 1R21EB030068-01
- **Recipient organization:** UNIVERSITY OF FLORIDA
- **Principal Investigator:** Jennifer A. Nichols
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $560,939
- **Award type:** 1
- **Project period:** 2020-09-01 → 2025-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10040078

## Citation

> US National Institutes of Health, RePORTER application 10040078, A Transfer Learning Framework for Creating Subject-Specific Musculoskeletal Models of the Hand (1R21EB030068-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10040078. Licensed CC0.

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