A deep learning-based method for grip strength prediction: Comparison of multilayer perceptron and polynomial regression approaches
The objective of this study was to accurately predict the grip strength using a deep learning-based method (e.g., multi-layer perceptron [MLP] regression). The maximal grip strength with varying postures (upper arm, forearm, and lower body) of 164 young adults (100 males and 64 females) were collect...
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| description | The objective of this study was to accurately predict the grip strength using a deep learning-based method (e.g., multi-layer perceptron [MLP] regression). The maximal grip strength with varying postures (upper arm, forearm, and lower body) of 164 young adults (100 males and 64 females) were collected. The data set was divided into a training set (90% of data) and a test set (10% of data). Different combinations of variables including demographic and anthropometric information of individual participants and postures was tested and compared to find the most predictive model. The MLP regression and 3 different polynomial regressions (linear, quadratic, and cubic) were conducted and the performance of regression was compared. The results showed that including all variables showed better performance than other combinations of variables. In general, MLP regression showed higher performance than polynomial regressions. Especially, MLP regression considering all variables achieved the highest performance of grip strength prediction (RMSE = 69.01N, R = 0.88, ICC = 0.92). This deep learning-based regression (MLP) would be useful to predict on-site- and individual-specific grip strength in the workspace to reduce the risk of musculoskeletal disorders in the upper extremity. |
| doi_str_mv | 10.1371/journal.pone.0246870 |
| format | Article |
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The maximal grip strength with varying postures (upper arm, forearm, and lower body) of 164 young adults (100 males and 64 females) were collected. The data set was divided into a training set (90% of data) and a test set (10% of data). Different combinations of variables including demographic and anthropometric information of individual participants and postures was tested and compared to find the most predictive model. The MLP regression and 3 different polynomial regressions (linear, quadratic, and cubic) were conducted and the performance of regression was compared. The results showed that including all variables showed better performance than other combinations of variables. In general, MLP regression showed higher performance than polynomial regressions. Especially, MLP regression considering all variables achieved the highest performance of grip strength prediction (RMSE = 69.01N, R = 0.88, ICC = 0.92). This deep learning-based regression (MLP) would be useful to predict on-site- and individual-specific grip strength in the workspace to reduce the risk of musculoskeletal disorders in the upper extremity.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0246870</identifier><identifier>PMID: 33571318</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Accuracy ; Algorithms ; Artificial neural networks ; Biology and Life Sciences ; Body measurements ; Computer and Information Sciences ; Computer vision ; Data analysis ; Deep learning ; Demographic aspects ; Dependent variables ; Editing ; Forecasts and trends ; Grip strength ; Health risks ; Independent variables ; Information processing ; Learning algorithms ; Long short-term memory ; Machine learning ; Measurement ; Mechanical engineering ; Medicine and Health Sciences ; Multilayer perceptrons ; Neural networks ; Physical Sciences ; Polynomials ; Posture ; Regression analysis ; Research and Analysis Methods ; Standard deviation ; Statistical analysis ; Systems engineering ; Variables ; Young adults</subject><ispartof>PloS one, 2021-02, Vol.16 (2), p.e0246870-e0246870</ispartof><rights>COPYRIGHT 2021 Public Library of Science</rights><rights>2021 Hwang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 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The maximal grip strength with varying postures (upper arm, forearm, and lower body) of 164 young adults (100 males and 64 females) were collected. The data set was divided into a training set (90% of data) and a test set (10% of data). Different combinations of variables including demographic and anthropometric information of individual participants and postures was tested and compared to find the most predictive model. The MLP regression and 3 different polynomial regressions (linear, quadratic, and cubic) were conducted and the performance of regression was compared. The results showed that including all variables showed better performance than other combinations of variables. In general, MLP regression showed higher performance than polynomial regressions. Especially, MLP regression considering all variables achieved the highest performance of grip strength prediction (RMSE = 69.01N, R = 0.88, ICC = 0.92). This deep learning-based regression (MLP) would be useful to predict on-site- and individual-specific grip strength in the workspace to reduce the risk of musculoskeletal disorders in the upper extremity.</description><subject>Accuracy</subject><subject>Algorithms</subject><subject>Artificial neural networks</subject><subject>Biology and Life Sciences</subject><subject>Body measurements</subject><subject>Computer and Information Sciences</subject><subject>Computer vision</subject><subject>Data analysis</subject><subject>Deep learning</subject><subject>Demographic aspects</subject><subject>Dependent variables</subject><subject>Editing</subject><subject>Forecasts and trends</subject><subject>Grip strength</subject><subject>Health risks</subject><subject>Independent variables</subject><subject>Information processing</subject><subject>Learning algorithms</subject><subject>Long short-term memory</subject><subject>Machine learning</subject><subject>Measurement</subject><subject>Mechanical 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One</addtitle><date>2021-02-11</date><risdate>2021</risdate><volume>16</volume><issue>2</issue><spage>e0246870</spage><epage>e0246870</epage><pages>e0246870-e0246870</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>The objective of this study was to accurately predict the grip strength using a deep learning-based method (e.g., multi-layer perceptron [MLP] regression). The maximal grip strength with varying postures (upper arm, forearm, and lower body) of 164 young adults (100 males and 64 females) were collected. The data set was divided into a training set (90% of data) and a test set (10% of data). Different combinations of variables including demographic and anthropometric information of individual participants and postures was tested and compared to find the most predictive model. The MLP regression and 3 different polynomial regressions (linear, quadratic, and cubic) were conducted and the performance of regression was compared. The results showed that including all variables showed better performance than other combinations of variables. In general, MLP regression showed higher performance than polynomial regressions. Especially, MLP regression considering all variables achieved the highest performance of grip strength prediction (RMSE = 69.01N, R = 0.88, ICC = 0.92). This deep learning-based regression (MLP) would be useful to predict on-site- and individual-specific grip strength in the workspace to reduce the risk of musculoskeletal disorders in the upper extremity.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>33571318</pmid><doi>10.1371/journal.pone.0246870</doi><tpages>e0246870</tpages><orcidid>https://orcid.org/0000-0003-4810-1014</orcidid><orcidid>https://orcid.org/0000-0002-4783-5860</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Accuracy Algorithms Artificial neural networks Biology and Life Sciences Body measurements Computer and Information Sciences Computer vision Data analysis Deep learning Demographic aspects Dependent variables Editing Forecasts and trends Grip strength Health risks Independent variables Information processing Learning algorithms Long short-term memory Machine learning Measurement Mechanical engineering Medicine and Health Sciences Multilayer perceptrons Neural networks Physical Sciences Polynomials Posture Regression analysis Research and Analysis Methods Standard deviation Statistical analysis Systems engineering Variables Young adults |
| title | A deep learning-based method for grip strength prediction: Comparison of multilayer perceptron and polynomial regression approaches |
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