The use of artificial neural networks to reduce data collection demands in determining spine loading: a laboratory based analysis.
Identifieur interne : 000E32 ( PubMed/Corpus ); précédent : 000E31; suivant : 000E33The use of artificial neural networks to reduce data collection demands in determining spine loading: a laboratory based analysis.
Auteurs : Robert J. Parkinson ; Jack P. CallaghanSource :
- Computer methods in biomechanics and biomedical engineering [ 1476-8259 ] ; 2009.
English descriptors
- KwdEn :
- MESH :
- physiology : Spine.
- Data Collection, Female, Humans, Lifting, Male, Models, Theoretical, Neural Networks (Computer).
Abstract
The extensive data requirements of three-dimensional inverse dynamics and joint modelling to estimate spinal loading prevent the implementation of these models in industry and may hinder development of advanced injury prevention standards. This work examines the potential of feed forward artificial neural networks (ANNs) as a data reduction approach and compared predictions to rigid link and EMG-assisted models. Ten males and ten females performed dynamic lifts, all approaches were applied and comparisons of predicted joint moments and joint forces were evaluated. While the ANN under- predicted peak extension moments (p = 0.0261) and joint compression (p < 0.0001), predictions of cumulative extension moments (p = 0.8293) and cumulative joint compression (p = 0.9557) were not different. Therefore, the ANNs proposed may be used to obtain estimates of cumulative exposure variables with reduced input demands; however they should not be applied to determine peak demands of a worker's exposure.
DOI: 10.1080/10255840902740620
PubMed: 19308871
Links to Exploration step
pubmed:19308871Le document en format XML
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Callaghan</name></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2009">2009</date><idno type="RBID">pubmed:19308871</idno><idno type="pmid">19308871</idno><idno type="doi">10.1080/10255840902740620</idno><idno type="wicri:Area/PubMed/Corpus">000E32</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Corpus" wicri:corpus="PubMed">000E32</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">The use of artificial neural networks to reduce data collection demands in determining spine loading: a laboratory based analysis.</title><author><name sortKey="Parkinson, Robert J" sort="Parkinson, Robert J" uniqKey="Parkinson R" first="Robert J" last="Parkinson">Robert J. Parkinson</name><affiliation><nlm:affiliation>Giffin Koerth Forensic Engineering and Science, Toronto, Ontario, Canada.</nlm:affiliation></affiliation></author><author><name sortKey="Callaghan, Jack P" sort="Callaghan, Jack P" uniqKey="Callaghan J" first="Jack P" last="Callaghan">Jack P. Callaghan</name></author></analytic><series><title level="j">Computer methods in biomechanics and biomedical engineering</title><idno type="eISSN">1476-8259</idno><imprint><date when="2009" type="published">2009</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Data Collection</term><term>Female</term><term>Humans</term><term>Lifting</term><term>Male</term><term>Models, Theoretical</term><term>Neural Networks (Computer)</term><term>Spine (physiology)</term></keywords><keywords scheme="MESH" qualifier="physiology" xml:lang="en"><term>Spine</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Data Collection</term><term>Female</term><term>Humans</term><term>Lifting</term><term>Male</term><term>Models, Theoretical</term><term>Neural Networks (Computer)</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">The extensive data requirements of three-dimensional inverse dynamics and joint modelling to estimate spinal loading prevent the implementation of these models in industry and may hinder development of advanced injury prevention standards. This work examines the potential of feed forward artificial neural networks (ANNs) as a data reduction approach and compared predictions to rigid link and EMG-assisted models. Ten males and ten females performed dynamic lifts, all approaches were applied and comparisons of predicted joint moments and joint forces were evaluated. While the ANN under- predicted peak extension moments (p = 0.0261) and joint compression (p < 0.0001), predictions of cumulative extension moments (p = 0.8293) and cumulative joint compression (p = 0.9557) were not different. Therefore, the ANNs proposed may be used to obtain estimates of cumulative exposure variables with reduced input demands; however they should not be applied to determine peak demands of a worker's exposure.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">19308871</PMID><DateCreated><Year>2010</Year><Month>02</Month><Day>25</Day></DateCreated><DateCompleted><Year>2010</Year><Month>06</Month><Day>01</Day></DateCompleted><DateRevised><Year>2010</Year><Month>02</Month><Day>25</Day></DateRevised><Article PubModel="Print"><Journal><ISSN IssnType="Electronic">1476-8259</ISSN><JournalIssue CitedMedium="Internet"><Volume>12</Volume><Issue>5</Issue><PubDate><Year>2009</Year><Month>Oct</Month></PubDate></JournalIssue><Title>Computer methods in biomechanics and biomedical engineering</Title><ISOAbbreviation>Comput Methods Biomech Biomed Engin</ISOAbbreviation></Journal><ArticleTitle>The use of artificial neural networks to reduce data collection demands in determining spine loading: a laboratory based analysis.</ArticleTitle><Pagination><MedlinePgn>511-22</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1080/10255840902740620</ELocationID><Abstract><AbstractText>The extensive data requirements of three-dimensional inverse dynamics and joint modelling to estimate spinal loading prevent the implementation of these models in industry and may hinder development of advanced injury prevention standards. This work examines the potential of feed forward artificial neural networks (ANNs) as a data reduction approach and compared predictions to rigid link and EMG-assisted models. Ten males and ten females performed dynamic lifts, all approaches were applied and comparisons of predicted joint moments and joint forces were evaluated. While the ANN under- predicted peak extension moments (p = 0.0261) and joint compression (p < 0.0001), predictions of cumulative extension moments (p = 0.8293) and cumulative joint compression (p = 0.9557) were not different. 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