Neural network and multiple linear regression to predict school children dimensions for ergonomic school furniture design

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.

► The paper uses neural networks and multiple linear regression techniques.
► To predict dimensions needed for ergonomic furniture design from easy-to-measure dimensions.
► Out of the five dimensions needed, four can be predicted from dimensions measured while standing.
► Results further show that neural networks outperforms multiple linear regression in general.