KITE, Toronto Rehabilitation Institute, University Health Network, 550 University Avenue, Toronto, ON, M5G 2A2, Canada.
Institute of Biomaterials and Biomedical Engineering, University of Toronto, 164 College Street. Room 407, Toronto, ON, M2S 3G9, Canada.
J Neuroeng Rehabil. 2020 Jul 14;17(1):97. doi: 10.1186/s12984-020-00728-9.
Parkinsonism is common in people with dementia, and is associated with neurodegenerative and vascular changes in the brain, or with exposure to antipsychotic or other dopamine antagonist medications. The detection of parkinsonian changes to gait may provide an opportunity to intervene and address reversible causes. In this study, we investigate the use of a vision-based system as an unobtrusive means to assess severity of parkinsonism in gait.
Videos of walking bouts of natural gait were collected in a specialized dementia unit using a Microsoft Kinect sensor and onboard color camera, and were processed to extract sixteen 3D and eight 2D gait features. Univariate regression to gait quality, as rated on the Unified Parkinson's Disease Rating Scale (UPDRS) and Simpson-Angus Scale (SAS), was used to identify gait features significantly correlated to these clinical scores for inclusion in multivariate models. Multivariate ordinal logistic regression was subsequently performed and the relative contribution of each gait feature for regression to UPDRS-gait and SAS-gait scores was assessed.
Four hundred one walking bouts from 14 older adults with dementia were included in the analysis. Multivariate ordinal logistic regression models incorporating selected 2D or 3D gait features attained similar accuracies: the UPDRS-gait regression models achieved accuracies of 61.4 and 62.1% for 2D and 3D features, respectively. Similarly, the SAS-gait models achieved accuracies of 47.4 and 48.5% with 2D or 3D gait features, respectively.
Gait features extracted from both 2D and 3D videos are correlated to UPDRS-gait and SAS-gait scores of parkinsonism severity in gait. Vision-based systems have the potential to be used as tools for longitudinal monitoring of parkinsonism in residential settings.
帕金森病在痴呆患者中很常见，与大脑的神经退行性和血管变化有关，或与抗精神病药物或其他多巴胺拮抗剂药物的暴露有关。检测帕金森步态变化可能提供干预和解决可逆原因的机会。在这项研究中，我们研究了使用基于视觉的系统作为一种非侵入性手段来评估步态帕金森病的严重程度。
使用 Microsoft Kinect 传感器和内置彩色摄像机在专门的痴呆病房中收集自然步态的行走片段，并对其进行处理以提取 16 个 3D 和 8 个 2D 步态特征。使用单变量回归分析步态质量与统一帕金森病评定量表 (UPDRS) 和辛普森-安格斯量表 (SAS) 的评分，以确定与这些临床评分显著相关的步态特征，将其纳入多变量模型。随后进行多元有序逻辑回归，评估每个步态特征对回归 UPDRS-步态和 SAS-步态评分的相对贡献。
纳入了 14 名痴呆老年人的 401 次行走发作的分析。纳入选定的 2D 或 3D 步态特征的多元有序逻辑回归模型达到了相似的准确性：2D 和 3D 特征的 UPDRS-步态回归模型的准确率分别为 61.4%和 62.1%。同样，SAS-步态模型的准确率分别为 2D 或 3D 步态特征的 47.4%和 48.5%。
从 2D 和 3D 视频中提取的步态特征与 UPDRS-步态和 SAS-步态评分的帕金森病严重程度相关。基于视觉的系统有可能成为住宅环境中监测帕金森病的纵向监测工具。
