Deep Learning and Procrustes Analysis for Early Dysgraphia Risk Detection with a Tablet Application - DATASET

Data to support the findings reported in the journal paper Lomurno, E., Dui, L. G., Gatto, M., Bollettino, M., Matteucci, M., & Ferrante, S. (2023). Deep Learning and Procrustes Analysis for Early Dysgraphia Risk Detection with a Tablet Application. Life, 13(3), 598. Caratterizzazione_y3b_pulito.xls:  Subject ID Age at the beginning of the study Handedness Sex Score_BVSCO_pulito.xls: Subject ID columns B-D: graphemes in the three tests of the BVSCO-2 columns E-G: z-score computed on normative data for the three tests of the BVSCO-2 median z-score: computed on columns E-G at risk: if median z-score is under the -2 threshold (0=not at risk, 1=at risk) executions.mat: a matrix for each time point (y1, y2a, y2b, y3a, y3b) and game (copa_q = copy square, copia_seq = copy sequence, tunnel_q = tunnel square, tunnel_ele = tunnel word). first column of all the cell arrays: subject ID second column for the copy games: three sub-matrices with an execution for each level (spontaneous, big, small) columns 2:N for the tunnel games: an average execution for each index of difficulty for each execution, the columns of the sub-matrices are normalized time, normalized x, normalized y and pressure   Copy games A matrix for each game and each time point with a number of rows equal to the number of children and a number of columns equal to 2: First column: array with school and child code Second column: executions For the two copy games, the data has a dimension of 3 x 1, where 3 are the modalities (spontaneous, big, small) and 1 is the data matrix. By accessing the data matrix we have four variables, i.e., normalized time, normalized x, normalized y and pressure.   Tunnel games A matrix for each game and each time point with a number of rows equal to the number of children and a number of columns equal to index of difficulty number +1: First column: array with school and child code From the second column onwards: executions, each column corresponds to the "typical" execution (the average) of an ID (5 for square tunnel and 8 for ele tunnel). Sub-matrices contain the data matrix with its four variables. i.e., normalized time, normalized x, normalized y and pressure.