Gallo-Aristizabal Jeferson David, Escobar-Grisales Daniel, Ríos-Urrego Cristian David, Vargas-Bonilla Jesús Francisco, García Adolfo M, Orozco-Arroyave Juan Rafael
GITA Lab., Faculty of Engineering, University of Antioquia, Medellín 510010, Colombia.
Cognitive Neuroscience Center, Universidad de San Andrés, Buenos Aires B1644BID, Argentina.
Diagnostics (Basel). 2025 Feb 5;15(3):381. doi: 10.3390/diagnostics15030381.
Parkinson's disease (PD) is the second most prevalent neurodegenerative disorder worldwide. People suffering from PD exhibit motor symptoms that affect the control of upper and lower limb movement. Among daily activities that depend on proper upper limb control is the handwriting process, which has been studied in state-of-the-art research, mainly considering non-semantic drawings like spirals, geometric figures, cursive lines, and others. This paper analyzes the suitability of modeling the handwriting process of digits from 0 to 9 to automatically discriminate between PD patients and healthy control subjects. The main hypothesis is that modeling these numbers allows a more natural evaluation of upper limb control. Two approaches are considered: modeling of the images resulting from the strokes collected by the digital tablet and modeling of the time series yielded by the digital tablet while performing the strokes, i.e., time-dependent signals. The first approach is implemented by fine-tuning a CNN-based architecture, while the second approach is based on hand-crafted features measured upon the time series, namely pressure and kinematic measurements. Features extracted from time-dependent signals are represented following two strategies, one based on statistical functionals and the other one based on creating Gaussian Mixture Models (GMMs). The experiments indicate that pressure-based features modeled with functionals are the ones that yield the highest accuracy, indicating that PD-related symptoms are better modeled with dynamic approaches than those based on images. The dynamic approach outperformed the image-based model, indicating that the writing process, modeled with signals collected over time, reveals motor symptoms more clearly than images resulting from handwriting. This finding is in line with previous results in the state-of-the-art research and constitutes a step forward to create more accurate and informative methods to detect and monitor PD symptoms.
帕金森病（PD）是全球第二常见的神经退行性疾病。帕金森病患者会出现影响上下肢运动控制的运动症状。在依赖上肢适当控制的日常活动中，书写过程是其中之一，在最新研究中已有对其进行研究，主要考虑的是诸如螺旋线、几何图形、草写线条等非语义绘图。本文分析了对0到9的数字书写过程进行建模以自动区分帕金森病患者和健康对照者的适用性。主要假设是对这些数字进行建模能够更自然地评估上肢控制能力。考虑了两种方法：对数字绘图板收集的笔画所产生的图像进行建模，以及对数字绘图板在执行笔画时产生的时间序列进行建模，即时间相关信号。第一种方法是通过微调基于卷积神经网络（CNN）的架构来实现，而第二种方法则基于对时间序列测量的手工制作特征，即压力和运动学测量。从时间相关信号中提取的特征按照两种策略进行表示，一种基于统计泛函，另一种基于创建高斯混合模型（GMM）。实验表明，用泛函建模的基于压力的特征具有最高的准确率，这表明与帕金森病相关的症状用动态方法建模比基于图像的方法更好。动态方法优于基于图像的模型，这表明用随时间收集的信号建模的书写过程比手写产生的图像更能清晰地揭示运动症状。这一发现与最新研究中的先前结果一致，并且朝着创建更准确、更具信息性的方法来检测和监测帕金森病症状迈出了一步。
