Neurodegenerative diseases, such as Alzheimer’s Disease or Parkinson’s disease, are unfortunately still incurable, although there are many therapies that can slow down the progression of the disease and improve patients’ lives. An essential condition, however, is the early diagnosis of these disorders to begin therapies as soon as possible: In fact, when the signs of the disease become evident, damages may be already significant and irreversible. In this context, it is generally agreed that handwriting is one of the first skills affected by the onset of cognitive disorders. For this reason, in a preliminary study, we considered a database of handwriting and drawing specimens and proposed a method for selecting the most relevant information for diagnosing neurodegenerative disorders. The basic idea was to generate, for each handwriting sample, a color image to exploit the ability of convolutional neural network to automatically extract features from raw images. In the generated images, the color of each elementary trait encodes, in the three RGB channels, the dynamic information associated with that trait. Starting from the very encouraging obtained results, the aim of this study is twofold: On the one hand, we have tried to improve the feature extraction phase, associating further dynamic information with each handwritten trait. On the other hand, we have expanded the database of handwriting samples by adding specimen derived from more complex drawing tasks. Finally, we carried out a large set of experiments for comparing the results obtained by using standard online features with those obtained with our feature extraction approach.

不幸的是，神经退行性疾病，如阿尔茨海默病或帕金森病，仍然无法治愈，尽管有许多疗法可以减缓疾病的进展并改善患者的生活。然而，一个基本条件是对这些疾病进行早期诊断，以便尽快开始治疗：事实上，当疾病的迹象变得明显时，损害可能已经很严重且不可逆转。在这种情况下，人们普遍认为，手写是受认知障碍发作影响的首批技能之一。出于这个原因，在一项初步研究中，我们考虑了一个手写和绘图样本数据库，并提出了一种选择最相关信息以诊断神经退行性疾病的方法。基本思想是为每个笔迹样本生成，利用卷积神经网络从原始图像中自动提取特征的能力的彩色图像。在生成的图像中，每个基本特征的颜色在三个 RGB 通道中编码与该特征相关的动态信息。从获得的非常令人鼓舞的结果开始，本研究的目的有两个：一方面，我们试图改进特征提取阶段，将进一步的动态信息与每个手写特征相关联。另一方面，我们通过添加源自更复杂绘图任务的样本来扩展手写样本的数据库。最后，我们进行了大量实验，将使用标准在线特征获得的结果与使用我们的特征提取方法获得的结果进行比较。