Many studies have shown how ergonomically designed furniture improves
productivity and well-being. As computers have become a part of students'
academic lives, they will grow further in the future. We propose
anthropometric-based furniture dimensions suitable for university students to
improve computer laboratory ergonomics. We collected data from 380 participants
and analyzed 11 anthropometric measurements, correlating them to 11 furniture
dimensions. Two types of furniture were studied: a non-adjustable chair with a
non-adjustable table and an adjustable chair with a non-adjustable table. The
mismatch calculation showed a significant difference between furniture
dimensions and anthropometric measurements. The one-way ANOVA test with a
significance level of 5% also showed a significant difference between proposed
and existing furniture dimensions. The proposed dimensions were found to be
more compatible and reduced mismatch percentages for both males and females
compared to existing furniture. The proposed dimensions of the furniture set
with adjustable seat height showed slightly improved results compared to the
non-adjustable furniture set. This suggests that the proposed dimensions can
improve comfort levels and reduce the risk of musculoskeletal disorders among
students. Further studies on the implementation and long-term effects of these
proposed dimensions in real-world computer laboratory settings are recommended.

通过对 380 名参与者的 11 项人体测量数据和 11 项家具尺寸进行分析，我们提出了适用于大学生的基于人体测量学的家具尺寸，旨在改善计算机实验室的人体工程学。研究发现，所提出的家具尺寸与现有的家具尺寸相比存在显著差异，而且对男性和女性来说，所提出的家具尺寸更加兼容和减少了不匹配的百分比。具有可调节座椅高度的家具集合的提出尺寸相对于不可调节家具集合还略有改善，这表明提出的家具尺寸可以提高舒适度并减少学生的肌肉骨骼疾病的风险。建议进一步研究在真实的计算机实验室环境中对这些提出尺寸的实施和长期效果。

优化大学计算机实验室人机工学：关于人体尺度测量、家具设计和 ANOVA 测试的研究

Optimizing Computer Lab Ergonomics in Universities: A Study on  Anthropometric Measurements, Furniture Design, and ANOVA Test

While methods that regress 3D human meshes from images have progressed
rapidly, the estimated body shapes often do not capture the true human shape.
This is problematic since, for many applications, accurate body shape is as
important as pose. The key reason that body shape accuracy lags pose accuracy
is the lack of data. While humans can label 2D joints, and these constrain 3D
pose, it is not so easy to "label" 3D body shape. Since paired data with images
and 3D body shape are rare, we exploit two sources of information: (1) we
collect internet images of diverse "fashion" models together with a small set
of anthropometric measurements; (2) we collect linguistic shape attributes for
a wide range of 3D body meshes and the model images. Taken together, these
datasets provide sufficient constraints to infer dense 3D shape. We exploit the
anthropometric measurements and linguistic shape attributes in several novel
ways to train a neural network, called SHAPY, that regresses 3D human pose and
shape from an RGB image. We evaluate SHAPY on public benchmarks, but note that
they either lack significant body shape variation, ground-truth shape, or
clothing variation. Thus, we collect a new dataset for evaluating 3D human
shape estimation, called HBW, containing photos of "Human Bodies in the Wild"
for which we have ground-truth 3D body scans. On this new benchmark, SHAPY
significantly outperforms state-of-the-art methods on the task of 3D body shape
estimation. This is the first demonstration that 3D body shape regression from
images can be trained from easy-to-obtain anthropometric measurements and
linguistic shape attributes. Our model and data are available at:
shapy.is.tue.mpg.de

通过采集互联网上的时尚模特图片和一小部分人体测量数据，以及用于各种三维身体网格的语言形状属性，我们训练了一种名为 SHAPY 的神经网络，该模型可从 RGB 图像中回归出三维人物的姿态以及形状。在新的 HBW 数据集上进行测试，SHAPY 在三维人体形状估计任务上表现出色，明显优于现有技术。