计算机视觉技术的出现和临床环境中视频摄像机的使用增加促进了运动障碍分析的进步。这篇综述调查了这些进步,提供了实用的,诊断和分析运动障碍的低成本解决方案,如帕金森病,共济失调,运动障碍,和Tourette综合征.运动障碍的传统诊断方法通常依赖于运动症状的主观评估,这带来了固有的挑战。此外,早期症状往往被忽视,而不同疾病的症状重叠会使早期诊断复杂化。因此,深度学习已被用于基于视频的运动障碍的客观分析。本研究系统地回顾了基于深度学习的运动障碍自动二维和三维视频分析的最新进展。我们通过搜索WebofScience,全面分析了直到2023年9月发表的文献,PubMed,Scopus,和Embase数据库。我们确定了68项相关研究,并提取了有关其目标的信息,数据集,模态,和方法论。这项研究旨在确定,目录,并提出了最重要的进步,提供关于视频分析和深度学习在运动障碍分析中的作用的综合知识库。首先,目标,包括特定的PD症状量化,共济失调评估,脑瘫评估,步态障碍分析,震颤评估,抽动检测(在图雷特综合征的背景下),肌张力障碍评估,并对异常运动识别进行了讨论。此后,对研究中使用的数据集进行了检查.随后,研究了与该主题相关的视频模式和深度学习方法。最后,数据集方面的挑战和机遇,可解释性,评价方法,和家庭/远程监控进行了讨论。
The advent of computer vision technology and increased usage of video cameras in clinical settings have facilitated advancements in movement disorder analysis. This
review investigated these advancements in terms of providing practical, low-cost solutions for the diagnosis and analysis of movement disorders, such as Parkinson\'s disease, ataxia, dyskinesia, and Tourette syndrome. Traditional diagnostic methods for movement disorders are typically reliant on the subjective assessment of motor symptoms, which poses inherent challenges. Furthermore, early symptoms are often overlooked, and overlapping symptoms across diseases can complicate early diagnosis. Consequently, deep learning has been used for the objective video-based analysis of movement disorders. This study systematically reviewed the latest advancements in automatic two-dimensional & three-dimensional video analysis using deep learning for movement disorders. We comprehensively analyzed the literature published until September 2023 by searching the Web of Science, PubMed, Scopus, and Embase databases. We identified 68 relevant studies and extracted information on their objectives, datasets, modalities, and methodologies. The study aimed to identify, catalogue, and present the most significant advancements, offering a consolidated knowledge base on the role of video analysis and deep learning in movement disorder analysis. First, the objectives, including specific PD symptom quantification, ataxia assessment, cerebral palsy assessment, gait disorder analysis, tremor assessment, tic detection (in the context of Tourette syndrome), dystonia assessment, and abnormal movement recognition were discussed. Thereafter, the datasets used in the study were examined. Subsequently, video modalities and deep learning methodologies related to the topic were investigated. Finally, the challenges and opportunities in terms of datasets, interpretability, evaluation methods, and home/remote monitoring were discussed.