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地面步态监测中单摄像头无标记系统的可靠性。

On the reliability of single-camera markerless systems for overground gait monitoring.

机构信息

Department of Computer Science, University of Verona, Strada Le Grazie, 15, Verona, 37134, Italy.

Department of Engineering for Innovation Medicine, University of Verona, Strada Le Grazie, 15, Verona, 37134, Italy.

出版信息

Comput Biol Med. 2024 Mar;171:108101. doi: 10.1016/j.compbiomed.2024.108101. Epub 2024 Feb 6.

Abstract

BACKGROUND AND OBJECTIVE

Motion analysis is crucial for effective and timely rehabilitative interventions on people with motor disorders. Conventional marker-based (MB) gait analysis is highly time-consuming and calls for expensive equipment, dedicated facilities and personnel. Markerless (ML) systems may pave the way to less demanding gait monitoring, also in unsupervised environments (i.e., in telemedicine). However,scepticism on clinical usability of relevant outcome measures has hampered its use. ML is normally used to analyse treadmill walking, which is significantly different from the more physiological overground walking. This study aims to provide end-users with instructions on using a single-camera markerless system to obtain reliable motion data from overground walking, while clinicians will be instructed on the reliability of obtained quantities.

METHODS

The study compares kinematics obtained from ML systems to those concurrently obtained from marker-based systems, considering different stride counts and subject positioning within the capture volume.

RESULTS

The findings suggest that five straight walking trials are sufficient for collecting reliable kinematics with ML systems. Precision on joint kinematics decreased at the boundary of the capture volume. Excellent correlation was found between ML and MB systems for hip and knee angles (0.92<R<0.96), with slightly lower correlations observed for ankle plantar-dorsiflexion. The Bland-Altman analysis indicated the largest bias for hip flexion/extension ([0.2,10.9]) and the smallest for knee joint ([0.1,0.8]) when comparing MB-PiG and MB-JC approaches. For MB-JC vs. ML-JC comparison, the largest bias was for the ankle joint ([1.2,11.8]), while the smallest was for the hip joint ([0.2,7.3]).

CONCLUSION

Single-camera markerless motion capture systems have great potential in assessing human joint kinematics during overground walking. Clinicians can confidently rely on estimated joint kinematics while walking, enabling personalized interventions and improving accessibility to remote evaluation and rehabilitation services, as long as: (i) the camera is positioned to capture someone walking back and forth at least five times with good visibility of the entire body silhouette; (ii) the walking path is at least 2 m long; and (iii) images captured at the boundaries of the camera image plane should be discarded.

摘要

背景与目的

运动分析对于患有运动障碍的人群进行有效且及时的康复干预至关重要。传统的基于标记物(MB)的步态分析非常耗时,并且需要昂贵的设备、专用的设施和人员。无标记物(ML)系统可能为更具挑战性的步态监测铺平道路,甚至在非监督环境中(即远程医疗)。然而,相关结果测量的临床可用性的怀疑阻碍了其使用。ML 通常用于分析跑步机行走,这与更生理的地面行走有很大的不同。本研究旨在为终端用户提供使用单摄像头无标记系统从地面行走中获得可靠运动数据的说明,同时指导临床医生获得的量的可靠性。

方法

本研究比较了从 ML 系统获得的运动学与同时从基于标记物系统获得的运动学,考虑了不同的步长计数和受试者在捕获体积内的定位。

结果

研究结果表明,使用 ML 系统收集可靠运动学数据,五次直线行走试验就足够了。关节运动学的精度在捕获体积的边界处降低。ML 系统与 MB 系统之间的髋部和膝部角度具有极好的相关性(0.92<R<0.96),而踝关节的相关性稍低。Bland-Altman 分析表明,在比较 MB-PiG 和 MB-JC 方法时,MB 系统的髋关节屈伸([0.2,10.9])和 MB 系统的膝关节([0.1,0.8])的偏差最大。对于 MB-JC 与 ML-JC 的比较,踝关节的偏差最大([1.2,11.8]),而髋关节的偏差最小([0.2,7.3])。

结论

单摄像头无标记运动捕捉系统在评估地面行走时人体关节运动学方面具有巨大潜力。临床医生可以在行走时自信地依赖估计的关节运动学,从而实现个性化干预,并提高对远程评估和康复服务的可及性,只要:(i)摄像机的位置能够捕捉到有人以良好的全身轮廓可见度来回行走至少五次;(ii)行走路径至少 2 米长;(iii)应丢弃摄像机图像平面边界处捕获的图像。

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