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Micrographia in Parkinson's Disease: Automatic Recognition through Artificial Intelligence. [PDF]

Mov Disord Clin Pract
Asci F, Saurio G, Pinola G, Falletti M, Zampogna A, Patera M, Fattapposta F, Scardapane S, Suppa A.   +8 more
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Connectome-based predictive modeling of handwriting and reading using task-evoked and resting-state functional connectivity. [PDF]

iScience
Li J, Zhang D, Ren H, Zhou K, Yang Y.
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Comprehensive search for assessment indicators that influence the level of handwriting difficulties among children in educational settings. [PDF]

Sci Rep
Takahata S, Hagihara H, Ishihara H, Enomoto D, Ienaga N, Noda H, Ishida S, Terayama K.   +7 more
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Parkinson disease detection based on in-air dynamics feature extraction and selection using machine learning. [PDF]

Sci Rep
Shin J, Miah ASM, Hirooka K, Hasan MAM, Maniruzzaman M.   +4 more
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Multi-modal deep learning framework for early detection of Parkinson's disease using neurological and physiological data for high-fidelity diagnosis. [PDF]

Sci Rep
Sar A, Puri PS, Naz H, Aich S, Choudhury T, Gabralla LA.   +5 more
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Decoding Handwriting Trajectories from Intracortical Brain Signals for Brain-to-Text Communication. [PDF]

Adv Sci (Weinh)
Xu G, Wang Z, Xu K, Zhu J, Zhang J, Wang Y, Hao Y.   +6 more
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Spanish primary students' writing attitudes and perceived family support: a socioemotional perspective. [PDF]

Front Psychol
Robledo P, Real Castelao S, Rodríguez C, Álvarez-Fernández ML.   +3 more
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Motor Aspects of Handwriting Acquisition and Developmental Dysgraphia. [PDF]

Children (Basel)
Jolly C.
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Synthesis for handwriting analysis

Pattern Recognition Letters, 2005
Recently a large number of studies has been published in the area of Fractal Analysis. In this paper we review briefly the IFS (Iterated Function System) theory, and we show how this theoretical tool leads to new applications in Pattern Recognition and more precisely in the analysis of handwritten texts.
N. Vincent, A. Seropian, G. Stamon
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