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Neural network and multiple linear regression to predict school children dimensions for ergonomic school furniture design.
Appl Ergon. 2012 Nov; 43(6):979-84.AE

Abstract

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.

Authors+Show Affiliations

School of Industrial Engineering, Islamic University-Gaza, Gaza Strip, Occupied Palestinian Territory. aghasr@yahoo.comNo affiliation info available

Pub Type(s)

Journal Article

Language

eng

PubMed ID

22365329

Citation

Agha, Salah R., and Mohammed J. Alnahhal. "Neural Network and Multiple Linear Regression to Predict School Children Dimensions for Ergonomic School Furniture Design." Applied Ergonomics, vol. 43, no. 6, 2012, pp. 979-84.
Agha SR, Alnahhal MJ. Neural network and multiple linear regression to predict school children dimensions for ergonomic school furniture design. Appl Ergon. 2012;43(6):979-84.
Agha, S. R., & Alnahhal, M. J. (2012). Neural network and multiple linear regression to predict school children dimensions for ergonomic school furniture design. Applied Ergonomics, 43(6), 979-84. https://doi.org/10.1016/j.apergo.2012.01.007
Agha SR, Alnahhal MJ. Neural Network and Multiple Linear Regression to Predict School Children Dimensions for Ergonomic School Furniture Design. Appl Ergon. 2012;43(6):979-84. PubMed PMID: 22365329.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Neural network and multiple linear regression to predict school children dimensions for ergonomic school furniture design. AU - Agha,Salah R, AU - Alnahhal,Mohammed J, Y1 - 2012/02/25/ PY - 2011/04/26/received PY - 2011/12/26/revised PY - 2012/01/30/accepted PY - 2012/2/28/entrez PY - 2012/3/1/pubmed PY - 2013/1/5/medline SP - 979 EP - 84 JF - Applied ergonomics JO - Appl Ergon VL - 43 IS - 6 N2 - 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. SN - 1872-9126 UR - https://www.unboundmedicine.com/medline/citation/22365329/Neural_network_and_multiple_linear_regression_to_predict_school_children_dimensions_for_ergonomic_school_furniture_design_ L2 - https://linkinghub.elsevier.com/retrieve/pii/S0003-6870(12)00021-X DB - PRIME DP - Unbound Medicine ER -