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Related Experiment Video

Updated: May 15, 2025

Handwriting Analysis Indicates Spontaneous Dyskinesias in Neuroleptic Naïve Adolescents at High Risk for Psychosis

Handwriting Analysis Indicates Spontaneous Dyskinesias in Neuroleptic Naïve Adolescents at High Risk for Psychosis

Published on: November 21, 2013

Analyzing handwriting legibility through hand kinematics.

Vahan Babushkin^1,2, Haneen Alsuradi¹, Muhamed Osman Al-Khalil³

¹Applied Interactive Multimedia Lab, Engineering Division, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates.

Frontiers in Artificial Intelligence

|April 10, 2025

Summary

This summary is machine-generated.

This study uses deep learning to analyze handwriting kinematics, finding that hand movements significantly improve legibility prediction accuracy. This approach aids in handwriting skill acquisition and pathology detection.

Keywords:

deep learning handwriting machine learning sensorimotor learning temporal convolutional networks

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Kinematic Analysis Using 3D Motion Capture of Drinking Task in People With and Without Upper-extremity Impairments

Published on: March 28, 2018

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Last Updated: May 15, 2025

Handwriting Analysis Indicates Spontaneous Dyskinesias in Neuroleptic Naïve Adolescents at High Risk for Psychosis

Handwriting Analysis Indicates Spontaneous Dyskinesias in Neuroleptic Naïve Adolescents at High Risk for Psychosis

Published on: November 21, 2013

Digital Handwriting Analysis of Characters in Chinese Patients with Mild Cognitive Impairment

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Published on: March 11, 2021

Kinematic Analysis Using 3D Motion Capture of Drinking Task in People With and Without Upper-extremity Impairments

Kinematic Analysis Using 3D Motion Capture of Drinking Task in People With and Without Upper-extremity Impairments

Published on: March 28, 2018

Area of Science:

Neurology
Biomedical Engineering
Computer Science

Background:

Handwriting is a complex motor skill influenced by various physiological and cognitive factors.
Assessing handwriting legibility is subjective and challenging due to expert variability.
Kinematic features of hand and stylus movements offer objective data for legibility analysis.

Purpose of the Study:

To develop a deep-learning model for analyzing kinematic features influencing handwriting legibility.
To identify key hand and stylus movement features critical for accurate legibility assessment.
To explore the application of this model in handwriting education and clinical diagnostics.

Main Methods:

A deep learning model based on temporal convolutional networks (TCN) was employed.
Fifty subjects performed a handwriting task, recording 117 spatiotemporal features of hand and stylus movements.
Shapley values were used to determine feature importance, with expert legibility scores for training and validation.

Main Results:

The model achieved approximately 76% accuracy using stylus kinematics alone.
Incorporating hand kinematics features improved model accuracy by about 10%.
Key features identified include pressure variability, pen slant (altitude, azimuth), and hand speed.

Conclusions:

Hand kinematics are crucial for accurate handwriting legibility assessment.
The deep learning model effectively identifies meaningful kinematic features related to legibility.
This technology can support personalized handwriting skill development and the detection of handwriting-related pathologies.