Alzheimer's disease is one of the most incisive illnesses among the
neurodegenerative ones, and it causes a progressive decline in cognitive
abilities that, in the worst cases, becomes severe enough to interfere with
daily life. Currently, there is no cure, so an early diagnosis is strongly
needed to try and slow its progression through medical treatments. Handwriting
analysis is considered a potential tool for detecting and understanding certain
neurological conditions, including Alzheimer's disease. While handwriting
analysis alone cannot provide a definitive diagnosis of Alzheimer's, it may
offer some insights and be used for a comprehensive assessment. The
Sigma-lognormal model is conceived for movement analysis and can also be
applied to handwriting. This model returns a set of lognormal parameters as
output, which forms the basis for the computation of novel and significant
features. This paper presents a machine learning approach applied to
handwriting features extracted through the sigma-lognormal model. The aim is to
develop a support system to help doctors in the diagnosis and study of
Alzheimer, evaluate the effectiveness of the extracted features and finally
study the relation among them.

通过 Sigma-lognormal 模型提取的手写特征，应用于机器学习，旨在开发一个支持系统来帮助医生诊断和研究阿尔茨海默病，并评估提取特征的有效性以及它们之间的关系。

通过对数正态特征分析机器学习方法研究阿尔茨海默病对书写的影响

A Machine Learning Approach to Analyze the Effects of Alzheimer's  Disease on Handwriting through Lognormal Features

The Kinematic Theory of rapid movements and its associated Sigma-Lognormal
model have been extensively used in a large variety of applications. While the
physical and biological meaning of the model have been widely tested and
validated for rapid movements, some shortcomings have been detected when it is
used with continuous long and complex movements. To alleviate such drawbacks,
and inspired by the motor equivalence theory and a conceivable visual feedback,
this paper proposes a novel framework to extract the Sigma-Lognormal
parameters, namely iDeLog. Specifically, iDeLog consists of two steps. The
first one, influenced by the motor equivalence model, separately derives an
initial action plan defined by a set of virtual points and angles from the
trajectory and a sequence of lognormals from the velocity. In the second step,
based on a hypothetical visual feedback compatible with an open-loop motor
control, the virtual target points of the action plan are iteratively moved to
improve the matching between the observed and reconstructed trajectory and
velocity. During experiments conducted with handwritten signatures, iDeLog
obtained promising results as compared to the previous development of the
Sigma-Lognormal.

本文提出了一个通过运动等效理论和可行的视觉反馈启发的新框架 iDeLog，用于提取 Sigma-Lognormal 模型参数。在与手写签名进行的实验中，iDeLog 相较于先前的 Sigma-Lognormal 发展取得了有希望的结果。