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Digital Handwriting Analysis of Characters in Chinese Patients with Mild Cognitive Impairment
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Handwriting in Mild Cognitive Impairment: Reliability Assessment and Machine Learning-Based Screening.

Simone Toffoli1, Carlo Abbate2, Francesca Lunardini3

  • 1Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy.

JMIR Aging
|September 23, 2025
PubMed
Summary
This summary is machine-generated.

Quantitative handwriting analysis using a sensorized pen offers a noninvasive method for early screening and monitoring of mild cognitive impairment (MCI). This approach can help detect MCI and track disease progression, potentially delaying dementia onset.

Keywords:
handwritingmachine learningmild cognitive impairmentparole-non-parole testsensorized ink pen

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Area of Science:

  • Neurology
  • Biomedical Engineering
  • Data Science

Background:

  • Mild cognitive impairment (MCI) is a significant risk factor for dementia, necessitating early detection and monitoring.
  • Current clinical tests for MCI have limitations, highlighting the need for objective, accessible tools.
  • Quantitative handwriting analysis presents a promising noninvasive method for MCI assessment.

Purpose of the Study:

  • To investigate the utility of quantitative handwriting analysis for noninvasive screening and monitoring of mild cognitive impairment (MCI).
  • To assess the reliability and correlation of handwriting indicators with clinical assessments in MCI patients.
  • To develop machine learning models for distinguishing MCI patients from healthy controls using handwriting data.

Main Methods:

  • Utilized a sensorized ink pen to record handwriting data during daily life tasks (grocery list, free text) and a clinical dictation test (parole-non-parole).
  • Computed 106 indicators related to time, fluency, force, and pen inclination from recorded data.
  • Analyzed test-retest reliability, correlated indicators with clinical scores, and built machine learning classifiers for MCI detection.

Main Results:

  • High reliability (93%) of indicators was observed for cursive handwriting, with moderate reliability (44%) for block letters.
  • Better temporal handwriting performance correlated with preserved cognitive status and daily function.
  • Machine learning models using free writing data achieved high accuracy (0.80-0.93) and F1-scores (0.81-0.92) in distinguishing MCI patients.

Conclusions:

  • Ecological handwriting analysis is suitable for comprehensive MCI monitoring, from early screening to tracking disease progression.
  • Sensorized pen technology provides objective, quantitative data for MCI assessment.
  • This noninvasive approach holds potential for widespread clinical application in dementia prevention strategies.