Computer aided diagnosis systems can provide non-invasive, low-cost tools to
support clinicians. These systems have the potential to assist the diagnosis
and monitoring of neurodegenerative disorders, in particular Parkinson's
disease (PD). Handwriting plays a special role in the context of PD assessment.
In this paper, the discriminating power of "dynamically enhanced" static images
of handwriting is investigated. The enhanced images are synthetically generated
by exploiting simultaneously the static and dynamic properties of handwriting.
Specifically, we propose a static representation that embeds dynamic
information based on: (i) drawing the points of the samples, instead of linking
them, so as to retain temporal/velocity information; and (ii) adding pen-ups
for the same purpose. To evaluate the effectiveness of the new handwriting
representation, a fair comparison between this approach and state-of-the-art
methods based on static and dynamic handwriting is conducted on the same
dataset, i.e. PaHaW. The classification workflow employs transfer learning to
extract meaningful features from multiple representations of the input data. An
ensemble of different classifiers is used to achieve the final predictions.
Dynamically enhanced static handwriting is able to outperform the results
obtained by using static and dynamic handwriting separately.

这篇论文研究了使用动态增强的静态手写图像来诊断帕金森病，通过同时利用手写的静态和动态属性，提出了一种嵌入动态信息的静态表示方法，通过在同一数据集上与现有方法进行了公平比较，证明了动态增强的静态手写图像能够胜过独立使用静态和动态手写的结果，从而实现了非侵入性、低成本的计算机辅助诊断系统来支持临床医生的诊断和监测神经退行性疾病，特别是帕金森病的评估。