Parkinson’s disease (PD) is commonly characterized by several motor symptoms, such as bradykinesia, akinesia, rigidity, and tremor. The analysis of patients’ fine motor control, particularly handwriting, is a powerful tool to support PD assessment. Over the years, various dynamic attributes of handwriting, such as pen pressure, stroke speed, in-air time, etc., which can be captured with the help of online handwriting acquisition tools, have been evaluated for the identification of PD. Motion events, and their associated spatio-temporal properties captured in online handwriting, enable effective classification of PD patients through the identification of unique sequential patterns. This paper proposes a novel classification model based on one-dimensional convolutions and Bidirectional Gated Recurrent Units (BiGRUs) to assess the potential of sequential information of handwriting in identifying Parkinsonian symptoms. One-dimensional convolutions are applied to raw sequences as well as derived features; the resulting sequences are then fed to BiGRU layers to achieve the final classification. The proposed method outperformed state-of-the-art approaches on the PaHaW dataset and achieved competitive results on the NewHandPD dataset.

帕金森氏病（PD）通常以几种运动症状为特征，例如运动迟缓，运动障碍，僵硬和震颤。分析患者的精细运动控制，尤其是手写体，是支持PD评估的强大工具。多年来，已经评估了手写的各种动态属性，例如笔压力，笔划速度，播音时间等，这些属性可以借助在线手写获取工具来捕获，以识别PD。运动事件及其在在线手写中捕获的相关时空特性可通过识别独特的连续模式来对PD患者进行有效分类。本文提出了一种基于一维卷积和双向门控递归单元（BiGRU）的新颖分类模型，以评估手写顺序信息在识别帕金森氏症中的潜力。一维卷积应用于原始序列以及派生特征。然后将得到的序列馈送到BiGRU层以实现最终分类。所提出的方法在PaHaW数据集上优于最新方法，在NewHandPD数据集上取得了竞争性结果。