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Related Concept Videos

Aging01:26

Aging

206
Aging is a complex biological phenomenon influenced by various processes that affect cellular and systemic functions. Several prominent theories attempt to explain its mechanisms, highlighting cellular limitations, oxidative damage, and hormonal changes as central factors in aging.
Cellular Clock Theory
The cellular clock theory posits that the human lifespan is closely tied to the finite capacity of cells to divide, a phenomenon governed by telomeres, which are protective caps at the ends of...
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The Effect of Aging on Tissues01:19

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Several body functions deteriorate with age. The external signs of aging are easily identifiable. For example, the skin becomes dry, less elastic, and thins out, forming wrinkles. The skin of the face begins to appear looser due to a decrease in the levels of elastic and collagen fibers in the connective tissue. Additionally, melanin production in the hair follicle decreases with age, resulting in gray hair. Moreover, the senses of sight and hearing decline, so glasses and hearing aids may...
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Related Experiment Video

Updated: Sep 22, 2025

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

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Handwriting Declines With Human Aging: A Machine Learning Study.

Francesco Asci1, Simone Scardapane2, Alessandro Zampogna3

  • 1IRCCS Neuromed Institute, Pozzilli, Italy.

Frontiers in Aging Neuroscience
|May 23, 2022
PubMed
Summary
This summary is machine-generated.

Handwriting analysis using artificial intelligence reveals age-related changes in stroke size. Machine learning algorithms can objectively detect these declines, offering potential for remote health monitoring.

Keywords:
agingconvolutional neural networkhandwritingmachine learningsmartphonetelemedicine

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Computer Science

Background:

  • Handwriting is a complex motor skill reflecting brain network activity.
  • It offers a non-invasive method for health status assessment.
  • Digital handwriting analysis via AI is promising for telemedicine.

Purpose of the Study:

  • To investigate age-related changes in handwriting using AI.
  • To assess the feasibility of remote handwriting analysis for health monitoring.

Main Methods:

  • 156 healthy adults were divided into younger, middle-aged, and older groups.
  • A smartphone-based handwriting task was digitalized.
  • DBNet algorithm analyzed stroke sizes, and a CNN classified samples.

Main Results:

  • Older adults exhibited significantly smaller average stroke sizes.
  • CNN achieved high accuracy in discriminating age groups (e.g., 77% for YA vs. OA).
  • Performance metrics like sensitivity, specificity, and AUC were reported for age-group classifications.

Conclusions:

  • Handwriting ability declines with aging.
  • Machine learning can objectively detect age-related handwriting changes.
  • This technology supports remote, objective health assessment.