仅用一组数据即可提取可解释的手部协同运动模式。
Alternating Minimization for Time-Shifted Synergy Extraction in Human Hand Coordination
- 联合优化协同模式与激活系数，避免分步处理。
- 在单组数据下实现高精度速度重建，协同模式紧凑。
- 适合运动控制研究与机器人手部建模应用。
从运动学数据中识别运动协同——即任务依赖时间偏移的协调手关节模式——是运动控制与机器人学的核心问题。现有两阶段方法需至少两组数据：先通过SVD提取候选波形，再用稀疏优化选择时移模板，导致数据采集复杂。本文提出一种基于优化的框架，联合学习少量协同模式及其稀疏激活系数。该方法对协同选择施加组稀疏性，对激活时间施加元素级稀疏性。我们设计了交替最小化算法，使不同任务间的系数更新解耦，协同更新简化为正则化最小二乘问题。本方法仅需一组数据，仿真结果表明能实现高精度速度重构，并生成紧凑、可解释的协同模式。
原文摘要 · Abstract (English)
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.
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