Automated Detection And Improvement Of Dysgraphia: Analyzing Handwriting Pressure With Sensor-Based Technique

Abstract

Dysgraphia is a learning disability that affects fine motor skills and can cause a hindrance in the academic growth of students if not treated and detected early. This work focuses on a novel Arduinobased approach to get pressure values using Force Sensitive Resistors (FSRs) written on a piece of paper placed on the board. The collected pressure values are analyzed on 68 people with dysgraphia to run a feedback loop using visual and audio signals making a reinforcement learning system such that if the pressure value is above the proposed threshold then the pressure applied and the writing angle needs to be changed. The threshold value is calculated after an extensive survey of students with dysgraphia by collecting their pressure values, analyzing them, and consulting an educational psychologist. This feedback loop helps to diagnose dysgraphia by providing quantifiable data and real-time feedback on improvement which can be viewed on a laptop or a phone. This approach offers a way to address problems with fine motor skills and improve academic performance. This method can help educational psychologists provide a tool to students for their improvement.