Department of Neurology, Mayo Clinic, Rochester, MN, USA.
Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA.
Sci Rep. 2024 Jul 29;14(1):17464. doi: 10.1038/s41598-024-68402-x.
Digital quantification of gait can be used to measure aging- and disease-related decline in mobility. Gait performance also predicts prognosis, disease progression, and response to therapies. Most gait analysis systems require large amounts of space, resources, and expertise to implement and are not widely accessible. Thus, there is a need for a portable system that accurately characterizes gait. Here, depth video from two portable cameras accurately reconstructed gait metrics comparable to those reported by a pressure-sensitive walkway. 392 research participants walked across a four-meter pressure-sensitive walkway while depth video was recorded. Gait speed, cadence, and step and stride durations and lengths strongly correlated (r > 0.9) between modalities, with root-mean-squared-errors (RMSE) of 0.04 m/s, 2.3 steps/min, 0.03 s, and 0.05-0.08 m for speed, cadence, step/stride duration, and step/stride length, respectively. Step, stance, and double support durations (gait cycle percentage) significantly correlated (r > 0.6) between modalities, with 5% RMSE for step and stance and 10% RMSE for double support. In an exploratory analysis, gait speed from both modalities significantly related to healthy, mild, moderate, or severe categorizations of Charleson Comorbidity Indices (ANOVA, Tukey's HSD, p < 0.0125). These findings demonstrate the viability of using depth video to expand access to quantitative gait assessments.
数字量化步态可用于测量与衰老和疾病相关的活动能力下降。步态表现也可预测预后、疾病进展和对治疗的反应。大多数步态分析系统需要大量的空间、资源和专业知识来实施，并且无法广泛获得。因此，需要一种能够准确描述步态的便携式系统。在这里，来自两个便携式摄像机的深度视频准确地重建了与压力感应式步道报告的步态指标相当的指标。392 名研究参与者在记录深度视频的同时在四米长的压力感应式步道上行走。步态速度、步频和步长及步幅持续时间和长度在两种模态之间具有很强的相关性（r > 0.9），均方根误差（RMSE）分别为 0.04 m/s、2.3 步/分钟、0.03 s 和 0.05-0.08 m，用于速度、步频、步长/步幅持续时间和步长/步幅长度。步长、站立和双支撑持续时间（步态周期百分比）在两种模态之间也具有显著相关性（r > 0.6），步长和站立的 RMSE 为 5%，双支撑的 RMSE 为 10%。在探索性分析中，两种模态的步态速度与 Charleson 共病指数的健康、轻度、中度或重度分类均显著相关（方差分析，Tukey 的 HSD，p < 0.0125）。这些发现表明，使用深度视频扩展定量步态评估的可实现性。
