Alternating Minimization for Time-Shifted Synergy Extraction in Human Hand Coordination

TL;DR

This paper presents a novel optimization framework for extracting motor synergies from hand kinematic data, enabling accurate and interpretable synergy identification with only a single dataset, unlike previous methods.

Contribution

It introduces an alternating minimization approach that jointly learns synergies and their sparse activations, reducing data requirements and improving interpretability.

Findings

Accurately reconstructs velocities using learned synergies.

Requires only a single dataset for synergy extraction.

Produces compact and interpretable synergy sets.

Abstract

Identifying motor synergies -- coordinated hand joint patterns activated at task-dependent time shifts -- from kinematic data is central to motor control and robotics. Existing two-stage methods first extract candidate waveforms (via SVD) and then select shifted templates using sparse optimization, requiring at least two datasets and complicating data collection. We introduce an optimization-based framework that jointly learns a small set of synergies and their sparse activation coefficients. The formulation enforces group sparsity for synergy selection and element-wise sparsity for activation timing. We develop an alternating minimization method in which coefficient updates decouple across tasks and synergy updates reduce to regularized least-squares problems. Our approach requires only a single data set, and simulations show accurate velocity reconstruction with compact, interpretable synergies.