{"id":3249,"date":"2022-04-21T04:06:30","date_gmt":"2022-04-21T04:06:30","guid":{"rendered":"https:\/\/chap.haifa.ac.il\/2022\/04\/21\/using-data-visualization-and-signal-processing-to-characterize-the-handwriting-process-2006\/"},"modified":"2022-04-21T04:10:19","modified_gmt":"2022-04-21T04:10:19","slug":"using-data-visualization-and-signal-processing-to-characterize-the-handwriting-process-2006","status":"publish","type":"post","link":"https:\/\/chap.haifa.ac.il\/en\/2022\/04\/21\/using-data-visualization-and-signal-processing-to-characterize-the-handwriting-process-2006\/","title":{"rendered":"Using data visualization and signal processing to characterize the handwriting process, 2006"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"3249\" class=\"elementor elementor-3249 elementor-3243\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-902b822 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"902b822\" data-element_type=\"section\" data-e-type=\"section\" data-settings=\"{&quot;jet_parallax_layout_list&quot;:[]}\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-836fcf3\" data-id=\"836fcf3\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-ef0577f elementor-widget elementor-widget-heading\" data-id=\"ef0577f\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Using data visualization and signal processing to characterize the handwriting process.<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-8cb2f14 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"8cb2f14\" data-element_type=\"section\" data-e-type=\"section\" data-settings=\"{&quot;jet_parallax_layout_list&quot;:[]}\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-9494177\" data-id=\"9494177\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-f401e3d elementor-widget elementor-widget-text-editor\" data-id=\"f401e3d\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p dir=\"ltr\" style=\"text-align: left;\">Rosenblum, S., Chevion, D., &amp; Weiss, P. L. (2006). Using data visualization and signal processing to characterize the handwriting process. Pediatric Rehabilitation, 9(4), 404-417.\u00a0<span class=\"identifier doi\"><a class=\"id-link\" href=\"https:\/\/doi.org\/10.1080\/13638490600667964\" target=\"_blank\" rel=\"noopener\" data-ga-category=\"full_text\" data-ga-action=\"DOI\">10.1080\/13638490600667964<\/a><\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-8319496 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"8319496\" data-element_type=\"section\" data-e-type=\"section\" data-settings=\"{&quot;jet_parallax_layout_list&quot;:[]}\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-13a98aa\" data-id=\"13a98aa\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-fadc4cd elementor-widget elementor-widget-text-editor\" data-id=\"fadc4cd\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<span data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;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\u20139 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 \u00bc 2.96, p \u00bc 0.008), letter height (t \u00bc 3.24, p \u00bc 0.005) and pen elevation (t \u00bc 2.91, p \u00bc 0.008). Significant differences were also found for the number of pen lifts (t \u00bc 2.27, p \u00bc 0.03), for the value of the correlation coefficients between letter length and time (t \u00bc \\u00016.62, p \u00bc 0.000) and between the actual and computed number of words (t \u00bc 2.79, p \u00bc 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.&quot;}\" data-sheets-userformat=\"{&quot;2&quot;:513,&quot;3&quot;:{&quot;1&quot;:0},&quot;12&quot;:0}\">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\u20139 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 \u00bc 2.96, p \u00bc 0.008), letter height (t \u00bc 3.24, p \u00bc 0.005) and pen elevation (t \u00bc 2.91, p \u00bc 0.008). Significant differences were also found for the number of pen lifts (t \u00bc 2.27, p \u00bc 0.03), for the value of the correlation coefficients between letter length and time (t \u00bc \u00016.62, p \u00bc 0.000) and between the actual and computed number of words (t \u00bc 2.79, p \u00bc 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.<\/span>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Using data visualization and signal processing to characterize the handwriting 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