Phase-separated CHPCA on single-subject hammer-strike pose data reveals a trunk-anchored global phase architecture with clear asymmetry between backswing and downswing phases.
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2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
ML models for baseball pitch speed prediction show poor cross-individual generalizability, with R-squared falling from 0.91 within individuals to 0.38 across individuals, and trunk/pivot leg kinematics providing the strongest transfer.
citing papers explorer
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Phase-Separated Complex Hilbert PCA on Markerless 3D Pose Estimation Data: A Global Phase Network and Its Extension to a Continuous Field on the Body Surface
Phase-separated CHPCA on single-subject hammer-strike pose data reveals a trunk-anchored global phase architecture with clear asymmetry between backswing and downswing phases.
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Cross-individual generalizability of machine learning models for ball speed prediction in baseball pitching
ML models for baseball pitch speed prediction show poor cross-individual generalizability, with R-squared falling from 0.91 within individuals to 0.38 across individuals, and trunk/pivot leg kinematics providing the strongest transfer.