Alternating Minimization for Time-Shifted Synergy Extraction in Human Hand Coordination
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909972317601792 |
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| author | Stepp, Trevor Olikkal, Parthan Vinjamuri, Ramana Anguluri, Rajasekhar |
| author_facet | Stepp, Trevor Olikkal, Parthan Vinjamuri, Ramana Anguluri, Rajasekhar |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_18206 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Alternating Minimization for Time-Shifted Synergy Extraction in Human Hand Coordination Stepp, Trevor Olikkal, Parthan Vinjamuri, Ramana Anguluri, Rajasekhar Robotics Optimization and Control 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. |
| title | Alternating Minimization for Time-Shifted Synergy Extraction in Human Hand Coordination |
| topic | Robotics Optimization and Control |
| url | https://arxiv.org/abs/2512.18206 |