An investigation of the Spatiotemporal Parameters of Gait and Margins of Stability throughout Adulthood - Dataset

This dataset is contains data on:- Anthropometric measurements: age (years), body mass (kg); body height (cm), leg length (mm) and BMI. - Gait parameters: walking speed (m/s), stride time (s), stride length (m), step time (s), cadence (steps/min), stance phase (%), double support phase (%), single support phase (%), step length (m) and step width (m).- Margins of Stability: Anterior-posterior Margin of Stability (mm), medio-lateral Margin of Stability (mm).Eligibility criteria for voluntary participants were adults between 20 and 89 years, categorized into seven decades. Participants were excluded if they had self-reported visual impairments, antalgic gait pattern, abnormal mobility in the lower limbs or any known neurological or orthopaedic disorder that could influence motor performance and balance. An instrumented gait analysis was performed at a movement analysis laboratory equipped with a three-dimensional motion capture system with eight cameras (Vicon T10, 100 Hz., ©Vicon Motion Systems Ltd., Oxford, UK, 100 fps, resolution 1 Megapixel (1120×896), 3 AMTI type OR 6–7 force plates (1000 fps, 46×50×8 cm) and 1 AccuGait® (1000 fps) force plate. Reflective markers were placed on anatomical landmarks on the subject’s body corresponding to the full body Plug-In-Gait model (30). Subjects walked barefoot over a 12-meter-long walkway at a self-selected walking speed, with the middle 6 meters used for data collection. In total, a minimum of six valid walking trials (i.e. visibility of all markers, presence of clean left and right heel strikes) were recorded. The data collection took place between April 2015 and January 2016. Marker trajectories were labelled using the Vicon Nexus 1.8.x software. Based on the force plate data and ankle marker trajectories, events of foot strike and foot off were determined. The gait cycle was calculated based on left and right heel marker trajectories. The total body centre of mass was calculated using the standard Vicon clinical model (Plug-In-Gait application for Nexus software). The .c3d-files were then exported to Matlab (R2017a for Windows) and through a custom written script the MoS was calculated according to the formulas of the extrapolated centre of mass as described by Hof et al. (2005) - The condition for dynamic stability, J Biomech (https://doi.org/10.1016/j.jbiomech.2004.03.025). The Margin of Stability was defined as the minimum distance between the centre of pressure and the extrapolated centre of mass along the medio-lateral and anterior-posterior axis during the single support phases.Spatiotemporal parameters were calculated from the left and right ankle marker trajectories through a custom written script in Matlab.本数据集包含以下三类数据： 1. 人体测量学指标：年龄（岁）、体质量（kg）、身高（cm）、腿长（mm）以及身体质量指数（Body Mass Index, BMI）； 2. 步态参数：步行速度（m/s）、步幅时间（s）、步幅长度（m）、步时（s）、步频（步/分钟）、支撑相占比（%）、双支撑相占比（%）、单支撑相占比（%）、步长（m）以及步宽（m）； 3. 稳定裕度（Margin of Stability）：前后向稳定裕度（mm）、内外向稳定裕度（mm）。 自愿受试者的入选标准为20至89岁的成年人，按每10年为一个年龄段划分为7个组别。若受试者自述存在视力障碍、痛性步态模式、下肢活动异常，或存在任何可能影响运动表现与平衡的已知神经或骨科疾病，则将其排除。 实验于配备三维运动捕捉系统的运动分析实验室开展，该系统包含8台Vicon T10相机（帧率100Hz，©Vicon Motion Systems Ltd., 英国牛津，100fps，分辨率1兆像素（1120×896））、3台AMTI OR 6–7型测力台（1000fps，尺寸46×50×8 cm）以及1台AccuGait®测力台（1000fps）。 按照全身Plug-In-Gait模型（参考文献30），将反光标记点粘贴于受试者体表的解剖标志点处。受试者赤足在12米长的步行通道上以自主选择的步行速度行走，取其中间6米区域进行数据采集。共记录至少6次有效步行试次（即所有标记点均可被捕捉，且左右足跟触地信号清晰）。数据采集时间为2015年4月至2016年1月。 使用Vicon Nexus 1.8.x软件对标记点轨迹进行标注。基于测力台数据与踝关节标记点轨迹，确定足跟触地与离地时刻。基于左右足跟标记点轨迹计算步态周期。采用Vicon标准临床模型（Nexus软件配套的Plug-In-Gait应用程序）计算全身质心。随后将.c3d格式文件导出至Matlab（Windows版R2017a），并通过自定义编写的Matlab脚本，按照Hof等人2005年发表于《Journal of Biomechanics》的外推质心公式（DOI: 10.1016/j.jbiomech.2004.03.025）计算稳定裕度。 稳定裕度定义为：单支撑相期间，压力中心与外推质心沿内外向与前后向轴的最小距离。 时空参数通过Matlab自定义脚本，基于左右踝关节标记点轨迹计算得到。