McGuirk Theresa E, Perry Elliott S, Sihanath Wandasun B, Riazati Sherveen, Patten Carolynn
Biomechanics, Rehabilitation, and Integrative Neuroscience Lab, Department of Physical Medicine and Rehabilitation, School of Medicine, University of California, Davis, Sacramento, CA, United States.
UC Davis Healthy Aging in a Digital World Initiative, a UC Davis "Big Idea", Sacramento, CA, United States.
Front Hum Neurosci. 2022 Jun 9;16:867485. doi: 10.3389/fnhum.2022.867485. eCollection 2022.
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 (ICC) and consistency (ICC). 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.
步态的三维（3D）运动学分析作为一种数字生物标志物，具有识别神经病理学、监测疾病进展以及通过表征步态障碍潜在机制来提供高分辨率结果指标以监测神经康复疗效的潜力。社区、临床和康复环境需要可使用的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）在这些环境中显示出作为一种可使用技术的前景。在本研究中，我们评估了在传统实验室环境之外实施3D MLMC技术以评估其作为神经康复结果评估工具的潜力的可行性。在3个月的时间里，对166名年龄在9至87岁（平均43.7岁，标准差20.4）、健康史各异的样本人群在社区的六个不同地点进行了评估。参与者以自我选择（SS）和最快舒适（FC）速度在地面行走。可行性措施考虑了该MLMC系统的扩展性、实施情况和实用性。样本人群的一个子集（46人）在MLMC的同时走过压力敏感人行道（PSW），以评估两个系统测量的时空步态参数的一致性。使用平均差异、布兰德-奥特曼分析以及一致性（ICC）和一致性（ICC）的组内相关系数比较了12个时空参数。所有测量结果均显示MLMC与PSW系统之间在步频、速度、步长步时、步幅长度和步幅时间方面具有良好至极佳的一致性，显示出很强的相似性。此外，这些信息可为针对步态功能障碍的康复策略制定提供参考。这些首次实验为在社区和临床实践环境中使用MLMC从不同人群获取可靠的3D运动学数据的可行性提供了证据。这项基础工作为未来使用MLMC的研究，尤其是其作为疾病进展和康复结果的数字生物标志物的应用奠定了基础。
