ArticleJournal of occupational health2021
Prediction and comparison of postural discomfort based on MLP and quadratic regression.
Article in Journal of occupational health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
2 citing papers in PubMed, 5 citations in OpenAlex.
- Prediction of Myocardial Infarction Using a Combined Generative Adversarial Network Model and Feature-Enhanced Loss Function.Metabolites · 2024Article
- Prediction and comparison of postural discomfort based on MLP and quadratic regression.Journal of occupational health · 2021Article
Corrections and comments
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Authors and funding
3 authors at 3 institutions in 2 countries.
Funding
Abstract
objectiveThe objective of this study was to predict postural discomfort based on the deep learning-based regression (multilayer perceptron [MLP] model).
methodsA 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.
resultsIn 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.
conclusionsThe 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.