Digital Handwriting Analysis of Characters in Chinese Patients with Mild Cognitive Impairment
March 11th, 2021
This study proposed a digital handwriting analysis of characters in individuals with mild cognitive impairment to find more information than is revealed by traditional pencil–paper handwriting analysis.
Handwriting analysis has been used to detect, cognitive and motor dysfunctions. Some aspect of handwriting difficulties can be an indicator for MCI, I'm delighted to(inaudible)The main advantage of this study is that it uses a digital handwriting analysis system for simplified Chinese character users. This method can also be applied to the detection of psychomotor retardation in patients with depression, Schizophrenia, or other psychiatric disorders who are simplified Chinese users.
Before beginning and analysis, select a 410 by 265 by 17.5 millimeter USB digitizer with a 3, 840 by 2, 160 dot spatial resolution, a 0.9 by 0.9 millimeter pixel size, a 30 millisecond temporal resolution, and an 8, 192 pressure level, and a handheld stylist pen for the handwriting movements. connect a laptop PC to the digitizer to collect and exhibit the handwriting traces. And install an appropriate data recording, processing, and analyzing software program.
Recruit mild cognitive impairment participants who present with a memory complaint, an objectively impaired memory function, intact activity of daily living, and the absence of Dementia. To perform a handwriting task, use the stylus to create sample Chinese characters in the writing area of the digitizer. Give the digitizer and stylus to the participant and instruct the participant to use the stylus to write their name in Chinese with the dominant hand, while holding the digitizer in a comfortable position.
Next, instruct the participant to use the dominant hand to write the printed version of the Chinese character, Xiang"If the character is being written in the wrong stroke order, stop the trial and trace and show the subject how to write the character in the correct stroke order. If any hesitation is derived from a lack of knowledge, stop the trial and show the participant how to write the character correctly. Then instruct the participant to use the dominant hand to sign their name in Chinese three times.
To analyze the handwriting task data, in the software analysis program, right click on experiment"to select properties, processing, and segmentation. In segmentation flags"click Add first segmentation at any rate"Add last segmentation at any rate"and Move segmentation point to nearest pen down if on a pen lift"In segmentation methods"click on At pen down trajectories"and reset the previously selected segmentation flags parameters. For parameter calculation in Xiang"select the participant and click Handwriting Trials"Use the tracing system to trace the handwriting process and stroke order of Xiang"step-by-step, and locate the segmentation of stroke number three of Xiang"The average absolute velocity will be automatically calculated within the extracted data.
Locate the segmentation of stroke number four of Zhang"The software will automatically calculate the average absolute velocity for strong number four. The pen pressure of each segmentation and average pen pressure will also be calculated by the software. Next, select the participant in Xiang, click handwriting trials and use the tracing system to trace the handwriting process and stroke order of the participant signature step-by-step.
Locate the segmentation of the stroke between the characters. The absolute size and road length will be calculated. Then use the formula to calculate the in-air length tortuosity in the segmentation between characters, As shown in the table, during the writing of the Chinese characters Xiang, elderly subjects with mild cognitive impairment exhibited a lower average absolute velocity of the number three, a number four strokes and a higher average pen pressure compared to healthy elderly subjects.
Additionally, during the signing of Chinese names, the elderly subjects with mild cognitive impairment exhibited a higher in-air length tortuosity in segmentation between the characters compared to healthy elderly subjects. The most important step is confirming the character legibility, using Xiang as example. The third stroke should be shorter than other horizontal strokes.
Foreign 00:05:35
View the full transcript and gain access to thousands of scientific videos
Overview
This study proposed a digital handwriting analysis of characters in individuals with mild cognitive impairment to find more information than is revealed by traditional pencil–paper handwriting analysis. The method can also be applied to detect psychomotor retardation in patients with various psychiatric disorders.
Key Study Components
Area of Science
- Neuroscience
- Psychiatry
- Digital Analysis
Background
- Handwriting analysis can indicate cognitive and motor dysfunctions.
- Traditional methods may not fully capture the nuances of handwriting difficulties.
- Digital systems offer enhanced analysis capabilities.
- This study focuses on simplified Chinese character users.
Purpose of Study
- To utilize digital handwriting analysis for better detection of mild cognitive impairment.
- To explore its application in psychiatric disorders.
- To improve diagnostic accuracy compared to traditional methods.
Methods Used
- Digital handwriting analysis system for simplified Chinese characters.
- Use of a USB digitizer with specific technical specifications.
- Connection to a laptop PC for data collection.
- Installation of data recording and analysis software.
Main Results
- The digital method provides more detailed handwriting traces.
- It can effectively identify signs of mild cognitive impairment.
- Applicable for other psychiatric conditions like depression and schizophrenia.
- Demonstrates the advantages of digital over traditional analysis.
Conclusions
- Digital handwriting analysis is a promising tool for cognitive assessment.
- It enhances the understanding of handwriting difficulties.
- Further research is needed to validate its effectiveness across different disorders.
Frequently Asked Questions
Executive Industry Relevance
Digital handwriting analysis offers a non-invasive, quantifiable approach to detect early motor-cognitive dysfunction in neurodegenerative and psychiatric conditions. By capturing fine-grained kinematic and pressure metrics during simplified Chinese character writing, the method supports target validation in preclinical screening of CNS disorders. Its applicability across depression, schizophrenia, and MCI enables mechanistic de-risking and predictive confidence in biomarker discovery pipelines.
Strategic Applications in Biopharma R&D
Early Discovery & Target Validation
- Scientific Value: Interrogates sensorimotor integration pathways linked to cognitive decline in MCI and psychiatric disorders.
- Operational Value: Enables objective, repeatable quantification of fine motor impairment as a translational biomarker.
Screening & Assay Development
- Scientific Value: Provides high-resolution temporal, spatial, and pressure data for assay standardization in neuropsychiatric screening.
- Operational Value: Supports scalable, digitized phenotyping platforms for cross-disorder comparative analysis.
Translational & Preclinical Research
- Scientific Value: Bridges motor phenotyping with cognitive endpoints to strengthen disease-relevant model validity.
- Operational Value: Facilitates longitudinal tracking of psychomotor changes in preclinical and clinical cohorts.
Pipeline & Workflow Integration
The method fits within early discovery workflows by generating quantifiable motor outputs that inform target engagement and pathway modulation studies in CNS drug development.
- Discovery Biology: Supports hypothesis testing of neural circuit dysfunction affecting motor control in MCI and psychiatric models.
- Screening: Delivers reproducible, digitized readouts for compound effect evaluation on fine motor performance.
- Analytics: Enables statistical comparison of velocity, pressure, and in-air trajectory metrics across experimental groups.
- Translational Research: Aligns motor biomarkers with cognitive outcomes to improve predictive validity in translational models.
- Enterprise Reuse: Represents a platform-agnostic tool for standardized motor assessment across therapeutic areas.
Operational & Enterprise Impact
- Scientific Value: Reduces mechanistic ambiguity by linking motor deficits to cognitive pathology in target validation.
- Operational Value: Ensures standardization and reproducibility through calibrated digitizer systems and software pipelines.
- Strategic Value: Improves go/no-go decisions by identifying CNS-active compounds with measurable motor effects early.
- Portfolio Impact: Enables risk-adjusted prioritization of targets based on multimodal biomarker profiles including motor function.
Implementation Considerations
- Expertise in neuropsychomotor assessment and digital signal processing.
- USB digitizer with sub-millimeter spatial and millisecond temporal resolution.
- Software capable of stroke segmentation, velocity, pressure, and tortuosity analysis.
- Standardized protocols for character selection, stroke order, and signing tasks in simplified Chinese.
- Need for age- and education-matched controls to isolate pathology-related motor changes.
