National Centre for Prosthetics and Orthotics, Department of Biomedical Engineering, University of Strathclyde, Glasgow, UK.
National Centre for Prosthetics and Orthotics, Department of Biomedical Engineering, University of Strathclyde, Glasgow, UK.
Gait Posture. 2024 Jul;112:95-107. doi: 10.1016/j.gaitpost.2024.04.029. Epub 2024 May 7.
Developments in vision-based systems and human pose estimation algorithms have the potential to detect, monitor and intervene early on neurodegenerative diseases through gait analysis. However, the gap between the technology available and actual clinical practice is evident as most clinicians still rely on subjective observational gait analysis or objective marker-based analysis that is time-consuming.
This paper aims to examine the main developments of vision-based motion capture and how such advances may be integrated into clinical practice.
The literature review was conducted in six online databases using Boolean search terms. A commercial system search was also included. A predetermined methodological criterion was then used to assess the quality of the selected articles.
A total of seventeen studies were evaluated, with thirteen studies focusing on gait classification systems and four studies on gait measurement systems. Of the gait classification systems, nine studies utilized artificial intelligence-assisted techniques, while four studies employed statistical techniques. The results revealed high correlations of gait features identified by classifier models with existing clinical rating scales. These systems demonstrated generally high classification accuracies and were effective in diagnosing disease severity levels. Gait measurement systems that extract spatiotemporal and kinematic joint information from video data generally found accurate measurements of gait parameters with low mean absolute errors, high intra- and inter-rater reliability.
Low cost, portable vision-based systems can provide proof of concept for the quantification of gait, expansion of gait assessment tools, remote gait analysis of neurodegenerative diseases and a point of care system for orthotic evaluation. However, certain challenges, including small sample sizes, occlusion risks, and selection bias in training models, need to be addressed. Nevertheless, these systems can serve as complementary tools, equipping clinicians with essential gait information to objectively assess disease severity and tailor personalized treatment for enhanced patient care.
基于视觉的系统和人体姿态估计算法的发展，有可能通过步态分析来检测、监测和早期干预神经退行性疾病。然而，可用技术与实际临床实践之间存在明显差距，因为大多数临床医生仍然依赖于主观观察性步态分析或耗时的基于客观标记的分析。
本文旨在研究基于视觉的运动捕捉的主要进展，以及这些进展如何整合到临床实践中。
使用布尔搜索词在六个在线数据库中进行文献综述，并包括对商业系统的搜索。然后使用预定的方法学标准来评估所选文章的质量。
共评估了十七项研究，其中十三项研究侧重于步态分类系统，四项研究侧重于步态测量系统。在步态分类系统中，有九项研究使用了人工智能辅助技术，四项研究使用了统计技术。结果表明，分类器模型识别的步态特征与现有临床评分量表高度相关。这些系统通常表现出较高的分类准确性，并有效地诊断疾病严重程度。从视频数据中提取时空和运动学关节信息的步态测量系统通常可以准确测量步态参数，平均绝对误差低，内部和外部评估者的可靠性高。
低成本、便携式基于视觉的系统可以为步态的量化、步态评估工具的扩展、神经退行性疾病的远程步态分析以及矫形器评估的护理点系统提供概念验证。然而，需要解决一些挑战，包括样本量小、遮挡风险以及训练模型中的选择偏差。尽管如此，这些系统可以作为补充工具，为临床医生提供必要的步态信息，以客观评估疾病严重程度并为增强患者护理量身定制个性化治疗。
