Avoiding spurious submovement decompositions II: a scattershot algorithm.

Brandon Rohrer; Neville Hogan

doi:10.1007/s00422-006-0055-y

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Avoiding spurious submovement decompositions II: a scattershot algorithm.

Brandon Rohrer·Neville Hogan

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Abstract

Evidence for the existence of discrete submovements underlying continuous human movement has motivated many attempts to "extract" them. Although they produce visually convincing results, all of the methodologies that have been employed are prone to produce spurious decompositions. In previous work, a branch-and-bound algorithm for submovement extraction, capable of global nonlinear minimization, and hence, capable of avoiding spurious decompositions, was presented [Rohrer and Hogan (Biol Cybern 39:190-199, 2003)]. Here, we present a scattershot-type global nonlinear minimization algorithm that requires approximately four orders of magnitude less time to compute. A sensitivity analysis reveals that the scattershot algorithm can reliably detect changes in submovement parameters over time, e.g., over the course of neuromotor recovery.

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Algorithms Biomechanical Phenomena Humans Models, Neurological Models, Theoretical Motor Activity Nonlinear Dynamics Psychomotor Performance Sensitivity and Specificity Journal Article Research Support, N.I.H., Extramural Research Support, U.S. Gov't, Non-P.H.S.

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Brandon Rohrer, Neville Hogan. 2006-03-29. Avoiding spurious submovement decompositions II: a scattershot algorithm.. https://doi.org/10.1007/s00422-006-0055-y

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