Sánchez-Fernández Luis Pastor, Garza-Rodríguez Alejandro, Sánchez-Pérez Luis Alejandro, Martínez-Hernández Juan Manuel
Centro de Investigación en Computación, Instituto Politécnico Nacional, Juan de Dios Bátiz Ave., México City 07738, Mexico.
Electrical and Computer Engineering Department, University of Michigan, 4901 Evergreen Rd, Dearborn, MI 48128, USA.
Bioengineering (Basel). 2023 May 13;10(5):588. doi: 10.3390/bioengineering10050588.
One problem in the quantitative assessment of biomechanical impairments in Parkinson's disease patients is the need for scalable and adaptable computing systems. This work presents a computational method that can be used for motor evaluations of pronation-supination hand movements, as described in item 3.6 of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS). The presented method can quickly adapt to new expert knowledge and includes new features that use a self-supervised training approach. The work uses wearable sensors for biomechanical measurements. We tested a machine-learning model on a dataset of 228 records with 20 indicators from 57 PD patients and eight healthy control subjects. The test dataset's experimental results show that the method's precision rates for the pronation and supination classification task achieved up to 89% accuracy, and the F1-scores were higher than 88% in most categories. The scores present a root mean squared error of 0.28 when compared to expert clinician scores. The paper provides detailed results for pronation-supination hand movement evaluations using a new analysis method when compared to the other methods mentioned in the literature. Furthermore, the proposal consists of a scalable and adaptable model that includes expert knowledge and affectations not covered in the MDS-UPDRS for a more in-depth evaluation.
帕金森病患者生物力学损伤定量评估中的一个问题是需要可扩展且适应性强的计算系统。这项工作提出了一种计算方法，可用于旋前 - 旋后手部运动的运动评估，如统一帕金森病评定量表（MDS - UPDRS）第3.6项所述。所提出的方法可以快速适应新的专家知识，并包含使用自监督训练方法的新特征。这项工作使用可穿戴传感器进行生物力学测量。我们在一个包含来自57名帕金森病患者和8名健康对照受试者的228条记录、20个指标的数据集上测试了一个机器学习模型。测试数据集的实验结果表明，该方法在旋前和旋后分类任务中的精确率达到了89%的准确率，并且在大多数类别中F1分数高于88%。与专家临床医生的评分相比，这些分数的均方根误差为0.28。与文献中提到的其他方法相比，本文提供了使用新分析方法进行旋前 - 旋后手部运动评估的详细结果。此外，该提议包括一个可扩展且适应性强的模型，该模型包含专家知识以及MDS - UPDRS未涵盖的影响因素，以进行更深入的评估。
