BACKGROUND Assessment and rating of Parkinson's Disease (PD) are commonly based on the medical observation of several clinical manifestations, including the analysis of motor activities. In particular, medical specialists refer to the MDS-UPDRS (Movement Disorder Society - sponsored revision of Unified Parkinson's Disease Rating Scale) that is the most widely used clinical scale for PD rating. However, clinical scales rely on the observation of some subtle motor phenomena that are either difficult to capture with human eyes or could be misclassified. This limitation motivated several researchers to develop intelligent systems based on machine learning algorithms able to automatically recognize the PD. Nevertheless, most of the previous studies investigated the classification between healthy subjects and PD patients without considering the automatic rating of different levels of severity. METHODS In this context, we implemented a simple and low-cost clinical tool that can extract postural and kinematic features with the Microsoft Kinect v2 sensor in order to classify and rate PD. Thirty participants were enrolled for the purpose of the present study: sixteen PD patients rated according to MDS-UPDRS and fourteen healthy paired subjects. In order to investigate the motor abilities of the upper and lower body, we acquired and analyzed three main motor tasks: (1) gait, (2) finger tapping, and (3) foot tapping. After preliminary feature selection, different classifiers based on Support Vector Machine (SVM) and Artificial Neural Networks (ANN) were trained and evaluated for the best solution. RESULTS Concerning the gait analysis, results showed that the ANN classifier performed the best by reaching 89.4% of accuracy with only nine features in diagnosis PD and 95.0% of accuracy with only six features in rating PD severity. Regarding the finger and foot tapping analysis, results showed that an SVM using the extracted features was able to classify healthy subjects versus PD patients with great performances by reaching 87.1% of accuracy. The results of the classification between mild and moderate PD patients indicated that the foot tapping features were the most representative ones to discriminate (81.0% of accuracy). CONCLUSIONS The results of this study have shown how a low-cost vision-based system can automatically detect subtle phenomena featuring the PD. Our findings suggest that the proposed tool can support medical specialists in the assessment and rating of PD patients in a real clinical scenario.

中文翻译：

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一种基于运动特征分析的低成本视觉系统，用于帕金森氏病的识别和严重性分级。

背景技术帕金森氏病（PD）的评估和评级通常基于对几种临床表现的医学观察，包括对运动活动的分析。特别是，医学专家参考了MDS-UPDRS（运动障碍协会-帕金森病疾病统一评分量表的赞助修订版），它是PD评分中使用最广泛的临床量表。但是，临床量表依赖于对一些细微的运动现象的观察，这些现象很难用人眼捕捉到或者可能被错误分类。这种局限性促使一些研究人员基于能够自动识别PD的机器学习算法开发智能系统。尽管如此，先前的大多数研究都对健康受试者和PD患者之间的分类进行了研究，而没有考虑对不同严重程度的自动评分。方法在这种情况下，我们实现了一种简单且低成本的临床工具，该工具可以使用Microsoft Kinect v2传感器提取姿势和运动学特征，以便对PD进行分类和评分。本研究目的招募了30名参与者：根据MDS-UPDRS评分的16名PD患者和14名健康配对对象。为了研究上半身和下半身的运动能力，我们获得并分析了三个主要的运动任务：（1）步态，（2）手指轻敲和（3）脚轻敲。初步选择功能后，对基于支持向量机（SVM）和人工神经网络（ANN）的不同分类器进行了训练和评估，以寻求最佳解决方案。结果关于步态分析，结果表明，ANN分类器在诊断PD方面只有9个特征，准确度达到89.4％，在PD严重度等级中只有6个特征，准确性达到95.0％，表现最佳。关于手指和脚的敲击分析，结果表明，使用提取的特征的SVM可以达到87.1％的准确度，从而对健康受试者和PD患者进行分类，表现出色。轻度和中度PD患者的分类结果表明，脚踏特征是最有代表性的特征（准确度为81.0％）。结论这项研究的结果表明，低成本的基于视觉的系统如何能够自动检测具有PD的细微现象。我们的发现表明，所提出的工具可以在实际临床场景中为医学专家评估PD患者提供支持。