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来自10至69岁人群的三维临床评级姿势数据。

Three-dimensional, clinically rated posture data from people aged 10 to 69 years.

作者信息

Dindorf Carlo, Ludwig Oliver, Fröhlich Michael

机构信息

Department of Sport Science, University of Kaiserslautern-Landau (RPTU), 67663 Kaiserslautern, Germany.

出版信息

Data Brief. 2024 Jul 6;55:110718. doi: 10.1016/j.dib.2024.110718. eCollection 2024 Aug.

Abstract

Weak posture is a widely recognized problem affecting individuals of all ages, that can lead to back pain, which is a significant socio-economic burden in civil societies. Posture assessment enables the early detection of postural deficiencies, thus enabling proactive interventions. Therefore, it is an important tool for promoting public health, not only in childhood and adolescence. This article provides posture data of 1,149 subjects aged 10 to 69 years measured by stereophotogrammetry. In addition to subject anthropometrics, raw sagittal posture parameters as well as calculated flèche cervicale (FC), flèche lombaire (FL), and kyphosis index (KI) and the respective values normalized to the trunk height (FC%, FL%, KI%) are provided. Further, based on the measurements and a visual inspection, biomedical experts made a classification of the presence of hyperkyphosis of the thoracic spine or hyperlordosis of the lumbar spine. In a second step, these assessments were algorithmically checked for label quality due to possible errors of the raters. Critical cases were reassessed by experts. In addition to the original ratings, these corrected labels are also reported. The data offers potential for data driven objective posture assessment and the development of diagnostic supportive machine learning applications.

摘要

不良体态是一个被广泛认可的问题,影响着各个年龄段的人群,它会导致背痛,而背痛在公民社会中是一项重大的社会经济负担。体态评估能够早期发现体态缺陷,从而实现积极干预。因此,它不仅在儿童期和青少年期,而且在促进公众健康方面都是一个重要工具。本文提供了通过立体摄影测量法对1149名年龄在10至69岁之间的受试者进行测量得到的体态数据。除了受试者的人体测量数据外,还提供了矢状面原始体态参数以及计算得出的颈椎矢状面弧(FC)、腰椎矢状面弧(FL)和驼背指数(KI),以及将各自的值归一化到躯干高度后的数值(FC%、FL%、KI%)。此外,基于测量结果和目视检查,生物医学专家对胸椎过度后凸或腰椎过度前凸的情况进行了分类。在第二步中,由于评分者可能存在的错误,对这些评估进行了算法标签质量检查。关键病例由专家重新评估。除了原始评分外,还报告了这些校正后的标签。这些数据为数据驱动的客观体态评估以及诊断支持性机器学习应用的开发提供了潜力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/be3b/11287015/b87542d0ffdf/gr1.jpg

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