Prediction and comparison of postural discomfort based on MLP and quadratic regression

• Published in: Journal of occupational health 2021-01, Vol.63 (1), p.e12292-n/a
• Main Authors: Lee, Jinwon, Hwang, Jaejin, Lee, Kyung‐Sun
• Format: Article
• Language: English
• Subjects: Adult; Algorithms; Classification; Datasets; Deep Learning; Discomfort; Ergonomics; Female; Gender; Humans; Machine learning; Male; multilayer perception; Multilayer perceptrons; Neural networks; Occupational health; Original; Pain - diagnosis; Performance prediction; postural discomfort; Posture; Predictions; Predictive Value of Tests; Psychophysics; regression; Regression Analysis; Regression models; Risk assessment; Risk Assessment - methods; Segments; Standard deviation; Variables
• ISSN: 1348-9585; 1341-9145; 1348-9585
• DOI: 10.1002/1348-9585.12292

Objective The objective of this study was to predict postural discomfort based on the deep learning‐based regression (multilayer perceptron [MLP] model). Methods A total of 95 participants performed 45 different static postures as a combination of 3 neck angles, 5 trunk angles, and 3 knee angles and rated the whole‐body discomfort. Two different combinations of variables including model 1 (all variables: gender, height, weight, exercise, body segment angles) and model 2 (gender, body segment angles) were tested. The MLP regression and a conventional regression (quadratic regression) were both conducted, and the performance was compared. Results In the overall regression analysis, the quadratic regression showed better performance than the MLP regression. For the postural discomfort group‐specific analysis, MLP regression showed greater performance than the quadratic regression especially in the high postural discomfort group. The MLP regression also showed better performance in predicting postural discomfort among individuals who had a variability of subjective rating among different postures compared to the quadratic regression. The deep learning for postural discomfort prediction would be useful for the efficient job risk assessment for various industries that involve prolonged static postures. Conclusions The deep learning for postural discomfort prediction would be useful for the efficient job risk assessment for various industries that involve prolonged static postures. This information would be meaningful as basic research data to study in predicting psychophysical data in ergonomics.