System and Method for Detecting Handwriting Problems

Abstract

A method for detecting handwriting problem, comprising:acquiring, by a handwriting instrument comprising one motion sensor, motion data while a user is using the handwriting instrument,analyzing the motion data by an artificial intelligence trained to detect a handwriting problem.

1 . A method for detecting handwriting problem, comprising:
 acquiring, by means of a handwriting instrument comprising at least one motion sensor, motion data while a user is using said handwriting instrument,   analyzing said motion data by an artificial intelligence trained to detect a handwriting problem.   
     
     
         2 . The method according to  claim 1 , wherein the artificial intelligence is a neural network. 
     
     
         3 . The method according to  claim 2 , the method further comprising a prior learning step comprising:
 acquiring a plurality of motion data from a plurality of persons using said handwriting instrument,   labelizing said acquired data,   using end-to-end supervised learning to train the neural network until it converges,   storing said neural network.   
     
     
         4 . The method according to  claim 3 , wherein the acquired data are classified in at least one of the following classes:
 type of grip on the handwriting instrument,   pressure applied on the handwriting instrument,   use of the handwriting instrument among writing, drawing or coloring,   fluidity of writing,   dyslexia,   dysgraphia,   wrong ductus.   
     
     
         5 . The method according to  claim 8 , further comprising acquiring vibration data by a stroke sensor, the method further comprising a prior learning step comprising:
 acquiring a plurality of motion data and vibration data from a plurality of persons using said handwriting instrument,   processing the vibration data to obtain stroke timestamps labels,   using supervised learning to train said neural network until it converges,   storing said neural network.   
     
     
         6 . The method according to  claim 5 , wherein the features extracted from the strokes timestamps comprise:
 total strokes duration,   total in air stroke duration,   strokes mean duration,   strokes mean and peak velocity,   number of pauses during use of the handwriting instrument,   ballistic index, which corresponds to an indicator of handwriting fluency which measures smoothness of the movement defined by the ratio between the number of zero crossings in the acceleration and the number of zero crossings in the velocity,   number of zero-crossing in the acceleration during strokes,   number of zero-crossing in the velocity during strokes.   
     
     
         7 . The method according to  claim 5 , wherein the extracted features of the stroke timestamps are classified in at least one of the following classes:
 type of grip on the handwriting instrument,   pressure applied on the handwriting instrument,   use of the handwriting instrument among writing, drawing or coloring,   fluidity of writing,   dyslexia,   dysgraphia,   wrong ductus.   
     
     
         8 . The method according to  claim 2 , wherein the neural network is further trained with a data base of letters and numbers correctly formed, a sequence of strokes and a direction of said strokes of the sequence of strokes being associated to each letter and number of the data base, and wherein, based on the motion and vibration data acquired during the use of the handwriting instrument, the neural network determines if the user is forming letters and numbers correctly.