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Updated: Jan 17, 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

轻度认知障碍的手写:可靠性评估和基于机器学习的选.

Simone Toffoli¹, Carlo Abbate², Francesca Lunardini³

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

|September 23, 2025

概括

使用传感笔进行定量手写分析,为轻度认知障碍 (MCI) 的早期查和监测提供了一种非侵入性方法. 这种方法可以帮助检测MCI和跟踪疾病的进展,可能延迟痴呆的发病.

关键词:

写字是用手写的机器学习是机器学习.轻度的认知障碍轻度的认知障碍假释-非假释测试试验有传感器的墨水笔.

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Last Updated: Jan 17, 2026

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

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科学领域:

神经学神经学
生物医学工程生物医学工程
数据科学数据科学数据科学

背景情况:

轻度认知障碍 (MCI) 是痴呆症的重要风险因素,需要早期检测和监测.
目前对MCI的临床试验存在局限性,这突显了对客观,可访问的工具的需求.
定量手写分析为MCI评估提供了一个有前途的非侵入性方法.

研究的目的:

调查定量手写分析对于轻度认知障碍 (MCI) 的非侵入性查和监测的实用性.
评估手写指标与MCI患者临床评估的可靠性和相关性.
开发机器学习模型,使用手写数据将MCI患者与健康对照区分开来.

主要方法:

在日常生活任务 (杂货清单,自由文本) 中使用传感器化墨水笔记录手写数据和临床 diktaction 测试 (缓刑-非缓刑).
从记录的数据中计算了106个与时间,流动性,力和笔倾斜相关的指标.
分析了测试重复测试的可靠性,与临床分数相关的指标,并构建了用于MCI检测的机器学习分类器.

主要成果:

标识符的可靠性高 (93%) 观察到形手写,适度可靠性 (44%) 块字母.
更好的时间手写表现与保存的认知状态和日常功能相关.
使用自由写入数据的机器学习模型在区分MCI患者时获得了高准确度 (0.80-0.93) 和F1-分数 (0.81-0.92).

结论:

生态手写分析适用于全面的MCI监测,从早期查到跟踪疾病进展.
传感笔技术为MCI评估提供客观,定量数据.
这种非侵入性方法有可能在痴呆症预防策略中得到广泛的临床应用.