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Digital Handwriting Analysis of Characters in Chinese Patients with Mild Cognitive Impairment
Published on: March 11, 2021
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Spatial and dynamical handwriting analysis in mild cognitive impairment.
Jacek Kawa1, Adam Bednorz2, Paula Stępień1
1Faculty of Biomedical Engineering, Silesian University of Technology, Roosevelta st. 40, Zabrze, Poland.
Computers in Biology and Medicine
|January 28, 2017
Summary
Handwriting analysis offers a fast and inexpensive method for diagnosing Mild Cognitive Impairment (MCI). Patients with MCI exhibit slower, larger handwriting and more variable writing dynamics compared to healthy individuals.
Area of Science:
- Neurology
- Biomedical Engineering
- Psychology
Background:
- Standard Mild Cognitive Impairment (MCI) assessment is time-consuming and requires specialists.
- Quantitative methods offer advantages over clinical judgment for MCI diagnosis.
- There is a need for fast, inexpensive, and reliable MCI diagnostic tools.
Purpose of the Study:
- To develop a diagnostic tool for MCI based on handwriting analysis.
- To identify handwriting parameters that differentiate MCI patients from healthy controls.
Main Methods:
- Collected handwriting samples from 37 MCI patients and 37 healthy controls using a Livescribe Echo Pen.
- Participants completed three writing tasks: regular writing, all-capital-letters writing, and single-letter repetition.
- Analyzed parameters such as writing speed, text size, and pause duration between strokes.
Main Results:
- MCI patients exhibited significantly slower writing times in regular and all-capital-letters tasks.
- Handwriting in the MCI group was noticeably larger across all tasks.
- MCI patients showed greater variation in writing dynamics, including longer pauses between strokes.
- The all-capital-letters task yielded the most discriminating features.
Conclusions:
- Quantitative handwriting analysis can significantly distinguish MCI patients.
- Incorporating handwriting analysis into psychological assessments could facilitate faster MCI diagnosis.
- This approach represents a promising step towards accessible MCI diagnostics.
