{"ID":2826671,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.18206","arxiv_id":"2512.18206","title":"Alternating Minimization for Time-Shifted Synergy Extraction in Human Hand Coordination","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.","short_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 t...","url_abs":"https://arxiv.org/abs/2512.18206","url_pdf":"https://arxiv.org/pdf/2512.18206v1","authors":"[\"Trevor Stepp\",\"Parthan Olikkal\",\"Ramana Vinjamuri\",\"Rajasekhar Anguluri\"]","published":"2025-12-20T04:09:37Z","proceeding":"cs.RO","tasks":"[\"cs.RO\",\"math.OC\"]","methods":"[]","has_code":false}