Journal: Scientific Reports

Article Title: Automated scoring for a Tablet-based Rey Figure copy task differentiates constructional, organisational, and motor abilities

doi: 10.1038/s41598-021-94247-9

Figure Lengend Snippet: Steps of the T-RCF system. (1) Administration: during the copy of the T-RCF (i.e., Tablet-based Rey complex figure), every pen-surface contact is recorded over time via a graphics tablet connected to a computer. (2) Classification: after administering the task, a classification program is adopted to assist the manual classification of the segmented strokes into the 48 basic elements of the T-RCF. (3) Pre-processing: in this stage, the digital recording of the copy is subjected to a transformation process aimed at eliminating global distortion of the copy output. (4A) Graphical outputs: the T-RCF system produces three graphical outputs: a reproduction of the final graphic result (i.e., graphic output), a visual representation of the velocity profile (i.e., velocity output), and a visual representation of the drawing sequence (procedure output). (4B) Performance indexes: finally, the T-RCF scoring system computes the 12 indices of performance. The figures are created using Matlab (version 2017b, The Mathworks, Natick, MA, USA, www.mathworks.com ) and Microsoft PowerPoint (Microsoft Office 365 Version 2105; www.office.com ).

Article Snippet: The ad-hoc software and any other code implementing algorithms described in the present paper are implemented in Matlab 2017b (The Mathworks, Natick, MA, USA; www.mathworks.com ), and employs functions from the following toolboxes: Optimization Toolbox (Version 8.0) , Signal Processing Toolbox (Version 7.5) , Statistics and Machine Learning Toolbox (Version 11.2) , and Image Processing Toolbox (Version 10.1) .

Techniques: Transformation Assay, Sequencing

Journal: Scientific Reports

Article Title: Automated scoring for a Tablet-based Rey Figure copy task differentiates constructional, organisational, and motor abilities

doi: 10.1038/s41598-021-94247-9

Figure Lengend Snippet: Graphical output of the T-RCF. The figure shows examples of graphical outputs for the T-RCF (i.e., Tablet-based Rey complex figure). ( A ) The graphical output for a participant achieving a high measure for all the composite scores. ( B ) The graphical output for a participant achieving a low SPA score. ( C ) The graphical output for a participant achieving a low PRO score. ( D ) The graphical output for a participant achieving a low KIN score. On the left of each one, graphical outputs of the final results are reported. In the middle, the graphical outputs of the velocity are reported. Each point of the strokes is coloured from light yellow (~ 0 velocities) to dark red (> 10 cm/s). On the right, the procedure outputs are reported. From top to bottom, the procedure outputs report: (a) the elapsed time (in seconds) at six different time frames equidistant along with the progression of the drawing (i.e., from the left to the right, when ~ 17%, ~ 33%, 50%, ~ 67%. ~ 83% and 100% of the total drawing length was reproduced). The time progression for the drawing phases only (i.e., pen-down time) is shown in brackets. (b) A coloured bar indicating the drawing progression according to the structure that the participant draws at each instant (i.e., base rectangle = GREEN; main substructures = BLUE; outer configurations = YELLOW; inner details = RED). (c) the drawing timeline panel showing the progress in the task at six time frames of the drawing (i.e., from the left to the right, when ~ 17%, ~ 33%, 50%, ~ 67%. ~ 83% and 100% of the total drawing length were reproduced). (d) A black and grey bar showing the drawing progression according to the relevance of the element that the participant draws in each drawing point (i.e., primary = BLACK; secondary = GRAY). (e) the drawing progression shown as a percentage from 0% (i.e., first pen-paper contact) to 100% (i.e., last pen-paper contact). The figure is created in Matlab (version 2017b, The Mathworks, Natick, MA, USA, www.mathworks.com ).

Article Snippet: The ad-hoc software and any other code implementing algorithms described in the present paper are implemented in Matlab 2017b (The Mathworks, Natick, MA, USA; www.mathworks.com ), and employs functions from the following toolboxes: Optimization Toolbox (Version 8.0) , Signal Processing Toolbox (Version 7.5) , Statistics and Machine Learning Toolbox (Version 11.2) , and Image Processing Toolbox (Version 10.1) .

Techniques: