Lognormality has proven to be an effective way for handwriting modeling. It assumes that handwriting is a time superimposition of a sequence of commands issued by the motor system, each command producing a stroke, i.e. a movement with a lognormal velocity profile. Motor control theories, moreover, suggest that handwriting movements result from both central and peripheral control, thus assuming that some movements of the sequence may not be encoded into the stored motor program but rather generated peripherally to keep the action as close as possible to the intended one. In the light of those observations, we present an algorithm for segmenting handwriting movements into strokes, each of which corresponds to a command stored into the motor program, while disregarding those that may depend on peripheral control. Experiments on handwriting samples show that the proposed algorithm detects the same number of strokes across multiple executions of a handwriting task by the same subject, and this set of strokes provides also a good reconstruction of the action.

Extracting the Motor Program of Handwriting from its Lognormal Representation

Parziale, Antonio
;
Marcelli, Angelo
2020

Abstract

Lognormality has proven to be an effective way for handwriting modeling. It assumes that handwriting is a time superimposition of a sequence of commands issued by the motor system, each command producing a stroke, i.e. a movement with a lognormal velocity profile. Motor control theories, moreover, suggest that handwriting movements result from both central and peripheral control, thus assuming that some movements of the sequence may not be encoded into the stored motor program but rather generated peripherally to keep the action as close as possible to the intended one. In the light of those observations, we present an algorithm for segmenting handwriting movements into strokes, each of which corresponds to a command stored into the motor program, while disregarding those that may depend on peripheral control. Experiments on handwriting samples show that the proposed algorithm detects the same number of strokes across multiple executions of a handwriting task by the same subject, and this set of strokes provides also a good reconstruction of the action.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11386/4763471
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