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Updated: Mar 3, 2026

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为美国儿童和青少年创建一个代表性的人类测量数据集
Matthew B Parkinson1, Matthew P Reed2
1Penn State University, University Park, PA, USA.
Applied ergonomics
|March 1, 2026
概括
这项研究提出了一种新方法,用于为美国儿童和青少年创建一个全面的人类测量数据集. 这些综合数据通过提供以前无法获得的关键百分位数数据来改进设计.
科学领域:
- 儿科人类学 儿科人类学
- 生物识别数据分析
- 人类因素工程 人类因素工程
背景情况:
- 美国儿童的公开可用的人体测量数据有限,并且很难用于设计.
- 现有的数据,比如国家健康和营养检查调查 (NHANES),需要复杂的权重,并且缺乏特定年龄组的细节.
- 设计的关键百分位数数据,特别是在极端,经常缺少当前的数据集.
研究的目的:
- 开发和验证一种合成美国儿童和青少年 (2-19岁) 未加权的人类测量数据集的方法.
- 为了解决产品和环境设计所需的详细人体测量数据的稀缺性.
- 为创建更新和更容易访问的设计相关数据集提供基础.
主要方法:
- 基于年龄和性别,为身高,体重和体重指数 (BMI) 的分布生成连续模型.
- 与2015-2018年NHANES数据的公布的非参数定量值对合成数据进行了验证.
- 专注于创建一个未加权的数据集,以在设计应用中实现更广泛的可用性.
主要成果:
- 综合的数据集显示出与已发表的NHANES量度对身高,体重和BMI的优异一致.
- 该方法成功地模拟了儿童和青少年广泛的年龄范围内的人类分布.
- 生成的数据填补了关键的下方和上方百分位数估计的空白.
结论:
- 经过验证的方法为美国儿童和青少年提供了强大的,合成的人类测量数据集.
- 这些综合数据是设计从业者需要详细的人类识别信息的宝贵资源.
- 该方法允许随着NHANES新数据的可用性不断更新,确保相关性.
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