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

2025-12-19

AI Summary

Hand motion synergy identification requires extracting time-shift-invariant coordinated joint patterns from kinematic data; however, conventional two-stage approaches rely on multiple datasets, resulting in complex pipelines and poor scalability. This paper proposes the first end-to-end alternating optimization framework that operates on a single hand kinematic dataset, jointly learning a small set of time-shift-invariant synergies and their sparse activation coefficients. By integrating group sparsity (for synergy selection) and element-wise sparsity (for temporal localization), the method unifies the modeling of synergy structure and dynamics, eliminating error propagation from sequential modeling. In simulation, the framework achieves high-fidelity reconstruction of hand velocity with minimal synergies and explicit time-shift relationships, markedly enhancing model interpretability and experimental deployability.

Technology Category

• Machine Learning: Learning with Manifolds
• Intelligent Robots: Manipulation
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization

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.

Problem

• Extract motor synergies from kinematic data
• Overcome two-stage method limitations requiring multiple datasets
• Jointly learn synergies and sparse activation coefficients

Innovation

• Jointly learns synergies and sparse activations
• Uses alternating minimization for optimization
• Requires only single dataset for extraction