一种使用正视图视频分析对帕金森步态严重程度进行自动分类的新方法。
A novel method for automatic classification of Parkinson gait severity using front-view video analysis.
机构信息
Centre for Artificial Intelligence, School of Information Technology, Halmstad University, Halmstad, Sweden.
Department of Computer Science, FAST-National University, Karachi, Pakistan.
出版信息
Technol Health Care. 2021;29(4):643-653. doi: 10.3233/THC-191960.
BACKGROUND
Gait impairment is an essential symptom of Parkinson's disease (PD).
OBJECTIVE
This paper introduces a novel computer-vision framework for automatic classification of the severity of gait impairment using front-view motion analysis.
METHODS
Four hundred and fifty-six videos were recorded from 19 PD patients using an RGB camera during clinical gait assessment. Gait performance in each video was rated by a neurologist using the unified Parkinson's disease rating scale for gait examination (UPDRS-gait). The proposed algorithm detects and tracks the silhouette of the test subject in the video to generate a height signal. Gait features were extracted from the height signal. Feature analysis was performed using the Kruskal-Wallis rank test. A support vector machine was trained using the features to classify the severity levels according to UPDRS-gait in 10-fold cross-validation.
RESULTS
Features significantly (p< 0.05) differentiated between median-ranks of UPDRS-gait levels. The SVM classified the levels with a promising area under the ROC of 80.88%.
CONCLUSION
Findings support the feasibility of this model for Parkinson's gait assessment in the home environment.
背景
步态障碍是帕金森病（PD）的一个基本症状。
目的
本文提出了一种新的计算机视觉框架，用于使用前视图运动分析自动分类步态障碍的严重程度。
方法
使用 RGB 摄像机从 19 名 PD 患者的 456 个视频中记录了在临床步态评估期间的运动情况。每位患者的步态表现均由神经病学家使用统一帕金森病评定量表中的步态检查部分（UPDRS-gait）进行评分。该算法检测并跟踪视频中测试对象的轮廓以生成高度信号。从高度信号中提取步态特征。使用 Kruskal-Wallis 秩检验进行特征分析。使用支持向量机（SVM）根据 10 倍交叉验证中 UPDRS-gait 的特征对严重程度进行分类。
结果
特征显著（p<0.05）区分了 UPDRS-gait 水平的中位数。SVM 对水平的分类具有 80.88%的 ROC 曲线下面积，这是一个很有前景的结果。
结论
这些发现支持了该模型在家用环境中进行帕金森步态评估的可行性。
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