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Using data visualization and signal processing to characterize the handwriting process.

Sara Rosenblum·Dan Chevion·Patrice L Tamar Weiss
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Abstract

INTRODUCTION: Disturbances in handwriting legibility and speed are found among elementary school-aged children. The aim of this paper is to present a set of sophisticated analytical tools suitable for visualization and evaluation of handwriting disturbances. METHODS: Handwriting samples from 30 children, 15 proficient and 15 non-proficient handwriters, aged 8-9 years were collected with the aid of a digitizing tablet. Temporal and spatial measures of the handwriting process dynamics based on signal processing methods were developed and visually presented. RESULTS: Significant differences between proficient and non-proficient handwriters were found in handwriting characteristics such as the standard deviations of letter width (t=2.96, p=0.008), letter height (t=3.24, p=0.005) and pen elevation (t=2.91, p=0.008). Significant differences were also found for the number of pen lifts (t=2.27, p=0.03), for the value of the correlation coefficients between letter length and time (t= -6.62, p=0.000) and between the actual and computed number of words (t=2.79, p=0.01). CONCLUSIONS: The techniques described in this paper provide objective measures for handwriting performance presented in a way designed to help clinicians and educators visualize handwriting difficulties during clinical evaluation and intervention. Data visualization and analysis appear to enhance information concerning the spatial and temporal dynamics of handwriting.

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AgraphiaChildDiagnosis, Computer-AssistedFemaleHandwritingHumansMalePsychomotor PerformanceSignal Processing, Computer-AssistedTask Performance and AnalysisJournal ArticleResearch Support, Non-U.S. Gov't

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Sara Rosenblum, Dan Chevion, Patrice L Tamar Weiss. Using data visualization and signal processing to characterize the handwriting process.. https://doi.org/10.1080/13638490600667964

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