Pappas Marc C, Boughanem David J, Baudendistel Sidney T, Chen Si, Liu Shuyu, Acevedo Gabriela T, Guarin Diego L
Annu Int Conf IEEE Eng Med Biol Soc. 2024 Jul;2024:1-4. doi: 10.1109/EMBC53108.2024.10781504.
Gait can be significantly impaired by neurological conditions such as Parkinson's disease (PD). Gait impairments can be quantified by using instrumented gait analysis techniques, but these instrumented techniques are expensive and suffer from limited accessibility in clinical practice. This paper introduces a method utilizing machine learning algorithms for video-based clinical gait analysis of frontal plane videos recorded with readily available devices. Our approach leverages open-source body pose (Google's MediaPipe) and metric depth (ZoeDepth) estimators to calculate spatiotemporal parameters (STP) of gait from videos and demonstrated high agreement and correlation with traditional marker-based motion capture (MoCap). We applied our proposed methodology to gait videos of healthy controls and people with PD (PWP). Our results agree with those obtained with high-end instrumental gait analysis techniques and support the detection of PD from gait videos.Clinical Relevance- This study presents an innovative, user-friendly, and cost-effective solution for clinical gait analysis. It will enable automated gait analysis via videos in gait-impaired individuals such as PWP, paving the way for telemedicine evaluation for movement decline. The future impact is early detection, effective remote monitoring, and new treatments for increased fall risk in an already mobility-challenged population.
步态可能会受到帕金森病（PD）等神经系统疾病的显著影响。可以使用仪器化步态分析技术来量化步态障碍，但这些仪器化技术成本高昂，且在临床实践中可及性有限。本文介绍了一种利用机器学习算法对使用现成设备录制的额面视频进行基于视频的临床步态分析的方法。我们的方法利用开源人体姿态（谷歌的MediaPipe）和度量深度（ZoeDepth）估计器从视频中计算步态的时空参数（STP），并证明与传统的基于标记的运动捕捉（MoCap）具有高度一致性和相关性。我们将提出的方法应用于健康对照者和帕金森病患者（PWP）的步态视频。我们的结果与高端仪器化步态分析技术获得的结果一致，并支持从步态视频中检测帕金森病。临床相关性——本研究为临床步态分析提供了一种创新、用户友好且具有成本效益的解决方案。它将使诸如帕金森病患者等步态受损个体能够通过视频进行自动步态分析，为运动功能下降的远程医疗评估铺平道路。未来的影响是早期检测、有效的远程监测以及针对已经行动不便人群中增加的跌倒风险的新治疗方法。
