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Biomechanical investigation of tasks concerning manual materials handling using response surface methodology

In typical manual material handling, the variations in walking pattern are decided by various factors, such as load being handled, frequency of handling, walking surface, etc. Traditional gait analysis protocols commonly evaluate individual factor within specified ranges associated with particular a...

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Autores principales: Adhaye, Amit M., Jolhe, Dhananjay A., Loyte, Akshay R., Devarajan, Yuvarajan, Thanappan, Subash
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10550981/
https://www.ncbi.nlm.nih.gov/pubmed/37794098
http://dx.doi.org/10.1038/s41598-023-43645-2
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author Adhaye, Amit M.
Jolhe, Dhananjay A.
Loyte, Akshay R.
Devarajan, Yuvarajan
Thanappan, Subash
author_facet Adhaye, Amit M.
Jolhe, Dhananjay A.
Loyte, Akshay R.
Devarajan, Yuvarajan
Thanappan, Subash
author_sort Adhaye, Amit M.
collection PubMed
description In typical manual material handling, the variations in walking pattern are decided by various factors, such as load being handled, frequency of handling, walking surface, etc. Traditional gait analysis protocols commonly evaluate individual factor within specified ranges associated with particular activities or pathologies. However, existing literature underscores the concurrent impact of multiple factors on gait. This study identifies five pivotal factors—walking speed, surface slope, load carried, carrying method, and footwear—as contributors to gait alterations. To address risk factors in manual material handling activities, we propose a unique design-of-experiment-based approach for multi-task gait analysis. Unraveling the relationship between manual handling attributes and human gait holds paramount importance in formulating effective intervention strategies. We optimized the five input factors across a cohort of 15 healthy male participants by employing a face-centered central composite design experimentation. A total of 29 input factor combinations were tested, yielding a comprehensive dataset encompassing 18 kinematic gait parameters (such as cadence, step length etc., measured using inertial measurement system), the isolated impacts of factors, and the interplay of two-factor interactions with corresponding responses. The results illuminate the optimal scenarios of input factors that enhance individual gait performance—these include wearing appropriate footwear, employing a backpack for load carriage, and maintaining a moderate walking pace on a medium slope with minimal load. The study identifies walking speed and load magnitude as primary influencers of gait mechanics, followed by the chosen carrying method. In consequence, the insights gained advocate for the refinement of manual material handling tasks based on the outcomes, effectively mitigating the risk of musculoskeletal disorders by suggesting the interventions for posture correction.
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spelling pubmed-105509812023-10-06 Biomechanical investigation of tasks concerning manual materials handling using response surface methodology Adhaye, Amit M. Jolhe, Dhananjay A. Loyte, Akshay R. Devarajan, Yuvarajan Thanappan, Subash Sci Rep Article In typical manual material handling, the variations in walking pattern are decided by various factors, such as load being handled, frequency of handling, walking surface, etc. Traditional gait analysis protocols commonly evaluate individual factor within specified ranges associated with particular activities or pathologies. However, existing literature underscores the concurrent impact of multiple factors on gait. This study identifies five pivotal factors—walking speed, surface slope, load carried, carrying method, and footwear—as contributors to gait alterations. To address risk factors in manual material handling activities, we propose a unique design-of-experiment-based approach for multi-task gait analysis. Unraveling the relationship between manual handling attributes and human gait holds paramount importance in formulating effective intervention strategies. We optimized the five input factors across a cohort of 15 healthy male participants by employing a face-centered central composite design experimentation. A total of 29 input factor combinations were tested, yielding a comprehensive dataset encompassing 18 kinematic gait parameters (such as cadence, step length etc., measured using inertial measurement system), the isolated impacts of factors, and the interplay of two-factor interactions with corresponding responses. The results illuminate the optimal scenarios of input factors that enhance individual gait performance—these include wearing appropriate footwear, employing a backpack for load carriage, and maintaining a moderate walking pace on a medium slope with minimal load. The study identifies walking speed and load magnitude as primary influencers of gait mechanics, followed by the chosen carrying method. In consequence, the insights gained advocate for the refinement of manual material handling tasks based on the outcomes, effectively mitigating the risk of musculoskeletal disorders by suggesting the interventions for posture correction. Nature Publishing Group UK 2023-10-04 /pmc/articles/PMC10550981/ /pubmed/37794098 http://dx.doi.org/10.1038/s41598-023-43645-2 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Adhaye, Amit M.
Jolhe, Dhananjay A.
Loyte, Akshay R.
Devarajan, Yuvarajan
Thanappan, Subash
Biomechanical investigation of tasks concerning manual materials handling using response surface methodology
title Biomechanical investigation of tasks concerning manual materials handling using response surface methodology
title_full Biomechanical investigation of tasks concerning manual materials handling using response surface methodology
title_fullStr Biomechanical investigation of tasks concerning manual materials handling using response surface methodology
title_full_unstemmed Biomechanical investigation of tasks concerning manual materials handling using response surface methodology
title_short Biomechanical investigation of tasks concerning manual materials handling using response surface methodology
title_sort biomechanical investigation of tasks concerning manual materials handling using response surface methodology
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10550981/
https://www.ncbi.nlm.nih.gov/pubmed/37794098
http://dx.doi.org/10.1038/s41598-023-43645-2
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