Feasibility of Markerless Motion Capture for Three-Dimensional Gait Assessment in Community Settings.
Feasibility of Markerless Motion Capture for Three-Dimensional Gait Assessment in Community Settings.
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DOI：
10.3389/fnhum.2022.867485
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发表时间：
2022
影响因子：
2.9
通讯作者：
中科院分区：
文献类型：
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作者：
步态的三维（3D）运动学分析有可能作为数字生物标志物来识别神经病理、监测疾病进展，并提供高分辨率的结果指标，通过表征步态损伤的潜在机制来监测神经康复疗效。社区、临床和康复环境需要可访问的3D运动捕捉技术。使用基于神经网络的深度学习算法的基于图像的无标记运动捕捉（MLMC）显示出在这些环境中作为一种可访问技术的前景。在这项研究中，我们评估了在传统实验室环境之外实施3D MLMC技术的可行性，以评估其作为神经康复结局评估工具的潜力。166名9-87岁的个体（平均43.7岁，S.D. 20.4）的各种健康史进行了评估，在6个不同的地点在社区超过3个月的时间。参与者以自选（SS）和最快舒适（FC）速度在地面上行走。可行性措施考虑了该MLMC系统的扩展、实施和实用性。一个子集的样本人群（46人）走在一个压力敏感的走道（PSW）同时与MLMC评估协议的时空步态参数测量两个系统之间。使用平均差异、Bland-Altman分析和组内相关系数比较12个时空参数的一致性（ICC 2，1）和一致性（ICC 3，1）。所有测量结果均显示MLMC和PSW系统之间具有良好至极好的一致性，其中节奏、速度、步长、步长时间、步长和步长时间显示出很强的相似性。此外，这些信息可以为针对步态功能障碍的康复策略的发展提供信息。这些第一个实验提供了在社区和临床实践环境中使用MLMC从不同人群中获取强大的3D运动学数据的可行性证据。这项基础性工作使MLMC的未来研究成为可能，特别是将其用作疾病进展和康复结果的数字生物标志物。
Three-dimensional (3D) kinematic analysis of gait holds potential as a digital biomarker to identify neuropathologies, monitor disease progression, and provide a high-resolution outcome measure to monitor neurorehabilitation efficacy by characterizing the mechanisms underlying gait impairments. There is a need for 3D motion capture technologies accessible to community, clinical, and rehabilitation settings. Image-based markerless motion capture (MLMC) using neural network-based deep learning algorithms shows promise as an accessible technology in these settings. In this study, we assessed the feasibility of implementing 3D MLMC technology outside the traditional laboratory environment to evaluate its potential as a tool for outcomes assessment in neurorehabilitation. A sample population of 166 individuals aged 9–87 years (mean 43.7, S.D. 20.4) of varied health history were evaluated at six different locations in the community over a 3-month period. Participants walked overground at self-selected (SS) and fastest comfortable (FC) speeds. Feasibility measures considered the expansion, implementation, and practicality of this MLMC system. A subset of the sample population (46 individuals) walked over a pressure-sensitive walkway (PSW) concurrently with MLMC to assess agreement of the spatiotemporal gait parameters measured between the two systems. Twelve spatiotemporal parameters were compared using mean differences, Bland-Altman analysis, and intraclass correlation coefficients for agreement (ICC2,1) and consistency (ICC3,1). All measures showed good to excellent agreement between MLMC and the PSW system with cadence, speed, step length, step time, stride length, and stride time showing strong similarity. Furthermore, this information can inform the development of rehabilitation strategies targeting gait dysfunction. These first experiments provide evidence for feasibility of using MLMC in community and clinical practice environments to acquire robust 3D kinematic data from a diverse population. This foundational work enables future investigation with MLMC especially its use as a digital biomarker of disease progression and rehabilitation outcome.
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DOI：
10.1155/2018/5830364
发表时间：
2018
期刊：
Parkinson's disease
影响因子：
--
作者：
Martinez HR;Garcia-Sarreon A;Camara-Lemarroy C;Salazar F;Guerrero-González ML
通讯作者：
Guerrero-González ML
DOI：
10.1111/j.1532-5415.2012.03927.x
发表时间：
2012-05-01
影响因子：
6.3
作者：
Blanco, Irene;Verghese, Joe;Derby, Carol A.
通讯作者：
Derby, Carol A.
影响因子：
2.4
作者：
Andriacchi, TP;Alexander, EJ
通讯作者：
Alexander, EJ
影响因子：
25
作者：
Mathis, Alexander;Mamidanna, Pranav;Bethge, Matthias
通讯作者：
Bethge, Matthias
影响因子：
2.4
作者：
Kanko, Robert M.;Laende, Elise K.;Deluzio, Kevin J.
通讯作者：
Deluzio, Kevin J.