School of Industrial Engineering, Islamic University-Gaza, Gaza Strip, Occupied Palestinian Territory.
Appl Ergon. 2012 Nov;43(6):979-84. doi: 10.1016/j.apergo.2012.01.007. Epub 2012 Feb 25.
The current study investigates the possibility of obtaining the anthropometric dimensions, critical to school furniture design, without measuring all of them. The study first selects some anthropometric dimensions that are easy to measure. Two methods are then used to check if these easy-to-measure dimensions can predict the dimensions critical to the furniture design. These methods are multiple linear regression and neural networks. Each dimension that is deemed necessary to ergonomically design school furniture is expressed as a function of some other measured anthropometric dimensions. Results show that out of the five dimensions needed for chair design, four can be related to other dimensions that can be measured while children are standing. Therefore, the method suggested here would definitely save time and effort and avoid the difficulty of dealing with students while measuring these dimensions. In general, it was found that neural networks perform better than multiple linear regression in the current study.
本研究旨在探讨在不测量全部人体尺寸的情况下，获取对校用家具设计至关重要的人体尺寸的可能性。本研究首先选择了一些易于测量的人体尺寸，然后使用两种方法来检查这些易于测量的尺寸是否可以预测对家具设计至关重要的尺寸。这两种方法是多元线性回归和神经网络。每个被认为对校用家具进行符合人体工程学设计必要的尺寸都被表示为一些其他测量的人体尺寸的函数。结果表明，在设计椅子所需的五个尺寸中，有四个可以与孩子们站立时可以测量的其他尺寸相关联。因此，这里提出的方法肯定会节省时间和精力，并避免在测量这些尺寸时与学生打交道的困难。总的来说，在本研究中发现神经网络比多元线性回归表现更好。
