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Updated: Jan 13, 2026

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

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

Published on: March 11, 2021

Essential Tremor Severity Assessment Using Handwriting Analysis and Machine Learning.

Jose Ignacio Sánchez Méndez^1,2, Elsa Fernandez^2,3, Alberto Bergareche⁴

¹NTT DATA EU & LATAM USA Branch Inc., 4100 North Fairfax Drive, Suite 810, Arlington, TX 22203, USA.

Sensors (Basel, Switzerland)

|January 10, 2026

Summary

This summary is machine-generated.

This study introduces a machine learning pipeline to accurately assess essential tremor (ET) severity using the Archimedes spiral test. The method offers a reliable tool for diagnosing ET and its progression, aiding clinical and telemedicine applications.

Keywords:

classification algorithms essential tremor handwriting analysis linear discriminant analysis machine learning personalized medicine principal component analysis support vector machines

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Area of Science:

Neurology
Biomedical Engineering
Data Science

Background:

Essential tremor (ET) is a common neurological disorder.
Accurate diagnosis and severity assessment are crucial for effective ET management.

Purpose of the Study:

To develop and validate a machine learning pipeline for assessing ET severity.
To utilize the Archimedes spiral test for objective tremor evaluation.
To establish a robust diagnostic tool for clinical and telemedicine use.

Main Methods:

A machine learning pipeline combining Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Support Vector Machines (SVMs).
Analysis of Archimedean spiral radius data from a family-based dataset spanning all ET severity levels.
Integration of the Fahn-Tolosa-Marin Tremor Rating Scale (FMT-TRS) for enhanced classification.

Main Results:

The pipeline effectively distinguishes between tremor presence and severity.
Robustness confirmed through cross-validation and Gaussian noise perturbation tests.
Demonstrated potential for non-invasive ET diagnosis and progression tracking.

Conclusions:

The machine learning pipeline shows significant potential as a non-invasive diagnostic tool for ET.
The framework combines geometric features, clinical scores, and machine learning for interpretable and clinically meaningful results.
This approach supports clinical practice and telemedicine applications for essential tremor management.