1,798 名健康和受伤受试者在跑步机上行走和跑步时的生物力学数据集。

IF 5.8 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES Scientific Data Pub Date : 2024-11-14 DOI:10.1038/s41597-024-04011-7
Reed Ferber, Allan Brett, Reginaldo K Fukuchi, Blayne Hettinga, Sean T Osis
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引用次数: 0

摘要

定量生物力学步态分析是伤病诊断和治疗的重要临床和研究工具。然而,一个主要的批评意见是,步态分析实验室大多是孤立运作的,缺乏可用于推进研究和统计方法的基准数据集。为了解决这个问题,我们提供了一个公开的生物力学数据集,其中包括 n = 1798 名健康和受伤的年轻和年长成年人在跑步机上以不同步速行走和/或跑步的数据。完整的数据集可在 Figshare+ 上获取,数据文件包含在一系列压缩文件夹中,文件夹名称代表受试者 ID。每个受试者 ID 文件夹都包含步行和/或跑步数据,其中包含原始标记轨迹数据以及每个受试者的元数据。此外,还提供了五个教程,演示了加载数据文件、离散变量样本分析、根据代码计算关节角度等方面的内容,并涵盖了更复杂的主题,如用于降维的主成分分析、统计参数映射以及进行无监督聚类。
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A Biomechanical Dataset of 1,798 Healthy and Injured Subjects During Treadmill Walking and Running.

Quantitative biomechanical gait analysis is an important clinical and research tool for injury and disease diagnosis and treatment. However, one major criticism is that gait analysis laboratories largely operate in isolation and there is a lack of benchmark datasets, which can be used to advance research and statistical methodologies. To address this, we present an open biomechanics dataset of n = 1798 healthy and injured, young and older adults during treadmill walking and/or running at a range of gait speeds. The full dataset is available on Figshare+ and data files are contained within a series of zipped folders with folder names representing the subject ID. Each subject ID folder contains walking and/or running data containing raw marker trajectory data along with metadata for each participant. Five tutorials are also provided, demonstrating aspects such as loading data files, sample analyses of discrete variables, and calculating joint angles from code along with covering more complex topics such as principal component analysis for dimensionality reduction, statistical parametric mapping, and conducting unsupervised clustering.

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来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
期刊介绍: Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
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