AI RESEARCH
Unsupervised clustering and classification of upper limb EMG signals during functional movements: a data-driven
arXiv CS.LG
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ArXi:2605.20599v1 Announce Type: new This study presents a comprehensive approach for the clustering and classification of upper-limb surface electromyography (sEMG) signals during functional reach and grasp movements. The methodology was applied to the NINAPRO DB4 dataset, which provides multichannel EMG recordings of 52 gestures. A four-stage pipeline was designed, including signal preprocessing, fea-ture extraction, gesture selection via hierarchical clustering, and comparative model evaluation.