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.
The proposed project aims to understand the biomechanical mechanisms underlying force production in the hand. Specifically, we utilize machine learning methods to examine how subject-specific differences influence hand force production and create subject-specific computer models from easy to obtain clinical data. The results, which integrate modeling with an individual?s clinical data, could enhance our ability to provide personalized diagnoses and prognoses for individuals suffering from hand impairments.
