A multivariate gaussian process factor model for hand shape during reach-to-grasp movements
Abstract
We propose a Multivariate Gaussian Process Factor Model to estimate low
dimensional spatio-temporal patterns of finger motion in repeated reach-to-grasp
movements. Our model decomposes and reduces the dimensionality of variation of the
multivariate functional data. We first account for time variability through multivariate
functional registration, then decompose finger motion into a term that is shared among
replications and a term that encodes the variation per replication. We discuss variants of our
model, estimation algorithms, and we evaluate its performance in simulations and in data
collected from a non-human primate executing a reach-to-grasp task. We show that by
taking advantage of the repeated trial structure of the experiments, our model yields an
intuitive way to interpret the time and replication variation in our kinematic dataset.
dimensional spatio-temporal patterns of finger motion in repeated reach-to-grasp
movements. Our model decomposes and reduces the dimensionality of variation of the
multivariate functional data. We first account for time variability through multivariate
functional registration, then decompose finger motion into a term that is shared among
replications and a term that encodes the variation per replication. We discuss variants of our
model, estimation algorithms, and we evaluate its performance in simulations and in data
collected from a non-human primate executing a reach-to-grasp task. We show that by
taking advantage of the repeated trial structure of the experiments, our model yields an
intuitive way to interpret the time and replication variation in our kinematic dataset.
Research Areas
