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健康成年人在虚拟现实环境中的仪器化跑步机上以三种不同速度行走的标准化三维步态数据。

Normative 3D gait data of healthy adults walking at three different speeds on an instrumented treadmill in virtual reality.

作者信息

Senden Rachel, Marcellis Rik, Willems Paul, Witlox Marianne, Meijer Kenneth

机构信息

Department of Physical Therapy, Maastricht University Medical Center (MUMC+), Maastricht, the Netherlands.

Department of Nutrition and Movement Sciences, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, the Netherlands.

出版信息

Data Brief. 2024 Feb 22;53:110230. doi: 10.1016/j.dib.2024.110230. eCollection 2024 Apr.

Abstract

A normative gait dataset of 246 healthy adults (122 men / 124 women, range in age 18-91 years, body weight 46.80-116.10 kg, height 1.53-1.97 m and BMI 18.25-35.63 kg/m2) is presented and publicly shared for three walking speed conditions. Raw and processed data are presented for each subject separately and for each walking speed, including data of every single step of both legs. The subject demographics and results from the physical examination are also presented which allows researchers and clinicians to create a self-selected reference group based on specific demographics. Besides the data per individual, data are also presented in age and gender groups. This provides a quick overview of healthy gait parameters which is relevant for use in clinical practice. Three dimensional gait analysis was performed at the Computer Assisted Rehabilitation Environment (CAREN) at the Maastricht University Medical Centre (MUMC+). Subjects walked on the instrumented treadmill surrounded with twelve 3D cameras, three 2D cameras and a virtual industrial environment projected on a 180° screen using the Human Body Lower Limb Model with trunk markers (HBM-II) as biomechanical model [1], [2]. Subjects walked at comfortable walking speed, 30% slower and 30% faster. These walking speed conditions were applied in a random sequence. Comfortable walking speed was determined using a RAMP protocol: subjects started to walk at 0.5m/s and every second the speed was increased with 0.01 m/s until the preferred speed was reached. The average of three repetitions was considered the comfortable speed. For each walking speed condition, 250 steps were recorded. The 3D gait data was collected using the D-flow CAREN software. For each subject, raw data of each walking speed condition is provided in .mox files, including the output from the model such as subject data (e.g. gender, body mass, knee and ankle width), center of mass (CoM), marker and force data, kinematic data (joint angles) and kinetic data (joint moments, ground reaction forces (GRFs) and joint powers) for each single step of both legs. Unfiltered and filtered data are included. C3D files with raw marker and GRF data were recorded in Nexus (Vicon software, version 2.8.1) and are available upon request. Raw data were processed in Matlab (Mathworks 2016), including quality check, step determination and the exportation of data to .xls files. For each adult and for each walking speed, an .xls file was created, containing spatiotemporal parameters, medio-lateral (ML) and back-forward (BF) margins of stability (MoS), 3D joint angles, anterior-posterior (AP) and vertical GRFs, 3D joint moments and 3D joint power of each step of both legs. Overview files per walking speed condition are created in .xls, presenting the averaged gait parameters (calculated as average over all valid steps) of every subject. The processed data is also presented and visualized per gender for different age groups (18-29 years, 30-39 years, 40-49 years, 50-59 years, 60-69 years, ≥70 years). This can serve as normative data for treadmill based 3D gait analyses in adults, applicable for clinical and research purposes. Data is available at OSF.io (https://osf.io/t72cw/).

摘要

本文展示了一个包含246名健康成年人(122名男性/124名女性,年龄范围18 - 91岁,体重46.80 - 116.10千克,身高1.53 - 1.97米,体重指数18.25 - 35.63千克/平方米)的规范步态数据集,并在三种步行速度条件下进行了公开共享。针对每个受试者以及每种步行速度,分别展示了原始数据和处理后的数据,包括双腿每一步的相关数据。同时还展示了受试者的人口统计学信息和体格检查结果,这使研究人员和临床医生能够基于特定人口统计学特征创建一个自我选择的参考组。除了个体数据外,还按年龄和性别组展示了数据。这提供了健康步态参数的快速概述,对临床实践具有参考价值。三维步态分析在马斯特里赫特大学医学中心(MUMC +)的计算机辅助康复环境(CAREN)中进行。受试者在配备仪器的跑步机上行走,周围环绕着十二个3D摄像头、三个2D摄像头,并且使用带有躯干标记的人体下肢模型(HBM - II)作为生物力学模型,在180°屏幕上投射出虚拟工业环境[1,2]。受试者以舒适步行速度、慢30%的速度和快30%的速度行走。这些步行速度条件以随机顺序应用。舒适步行速度通过RAMP协议确定:受试者从0.5米/秒开始行走,每秒速度增加0.01米/秒,直到达到首选速度。三次重复的平均值被视为舒适速度。对于每种步行速度条件,记录250步。使用D - flow CAREN软件收集三维步态数据。对于每个受试者,每种步行速度条件的原始数据以.mox文件形式提供,包括模型输出,如受试者数据(如性别、体重、膝盖和脚踝宽度)、质心(CoM)、标记和力数据、运动学数据(关节角度)以及双腿每一步的动力学数据(关节力矩、地面反作用力(GRFs)和关节功率)。包括未滤波和滤波后的数据。带有原始标记和GRF数据的C3D文件记录在Nexus(Vicon软件,版本2.8.1)中,可根据要求提供。原始数据在Matlab(Mathworks 2016)中进行处理,包括质量检查、步长确定以及将数据导出到.xls文件。针对每个成年人和每种步行速度,创建了一个.xls文件,其中包含时空参数、稳定性的中外侧(ML)和前后(BF)边缘(MoS)、三维关节角度、前后(AP)和垂直GRFs、三维关节力矩以及双腿每一步的三维关节功率。针对每种步行速度条件创建了概述文件,以.xls形式呈现每个受试者的平均步态参数(计算为所有有效步长的平均值)。还按性别针对不同年龄组(18 - 29岁、30 - 39岁、40 - 49岁、50 - 59岁、60 - 69岁、≥70岁)展示并可视化了处理后的数据。这可作为基于跑步机的成人三维步态分析的规范数据,适用于临床和研究目的。数据可在OSF.io(https://osf.io/t72cw/)获取。

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