Leveraging knowledge from physiological data: on-body heat stress risk prediction with sensor networks.
Identifieur interne : 000761 ( PubMed/Corpus ); précédent : 000760; suivant : 000762Leveraging knowledge from physiological data: on-body heat stress risk prediction with sensor networks.
Auteurs : Elena Gaura ; John Kemp ; James BruseySource :
- IEEE transactions on biomedical circuits and systems [ 1940-9990 ] ; 2013.
English descriptors
- KwdEn :
- Accelerometry, Adolescent, Adult, Algorithms, Clothing, Decision Support Systems, Clinical (instrumentation), Equipment Design, Firefighters, Heat Stress Disorders (diagnosis), Heat Stress Disorders (prevention & control), Humans, Male, Models, Statistical, Remote Sensing Technology (instrumentation), Remote Sensing Technology (methods), Reproducibility of Results, Skin Temperature (physiology), Young Adult.
- MESH :
- diagnosis : Heat Stress Disorders.
- instrumentation : Decision Support Systems, Clinical, Remote Sensing Technology.
- methods : Remote Sensing Technology.
- physiology : Skin Temperature.
- prevention & control : Heat Stress Disorders.
- Accelerometry, Adolescent, Adult, Algorithms, Clothing, Equipment Design, Firefighters, Humans, Male, Models, Statistical, Reproducibility of Results, Young Adult.
Abstract
The paper demonstrates that wearable sensor systems, coupled with real-time on-body processing and actuation, can enhance safety for wearers of heavy protective equipment who are subjected to harsh thermal environments by reducing risk of Uncompensable Heat Stress (UHS). The work focuses on Explosive Ordnance Disposal operatives and shows that predictions of UHS risk can be performed in real-time with sufficient accuracy for real-world use. Furthermore, it is shown that the required sensory input for such algorithms can be obtained with wearable, non-intrusive sensors. Two algorithms, one based on Bayesian nets and another on decision trees, are presented for determining the heat stress risk, considering the mean skin temperature prediction as a proxy. The algorithms are trained on empirical data and have accuracies of 92.1±2.9% and 94.4±2.1%, respectively when tested using leave-one-subject-out cross-validation. In applications such as Explosive Ordnance Disposal operative monitoring, such prediction algorithms can enable autonomous actuation of cooling systems and haptic alerts to minimize casualties.
DOI: 10.1109/TBCAS.2013.2254485
PubMed: 24473550
Links to Exploration step
pubmed:24473550Le document en format XML
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The work focuses on Explosive Ordnance Disposal operatives and shows that predictions of UHS risk can be performed in real-time with sufficient accuracy for real-world use. Furthermore, it is shown that the required sensory input for such algorithms can be obtained with wearable, non-intrusive sensors. Two algorithms, one based on Bayesian nets and another on decision trees, are presented for determining the heat stress risk, considering the mean skin temperature prediction as a proxy. The algorithms are trained on empirical data and have accuracies of 92.1±2.9% and 94.4±2.1%, respectively when tested using leave-one-subject-out cross-validation. In applications such as Explosive Ordnance Disposal operative monitoring, such prediction algorithms can enable autonomous actuation of cooling systems and haptic alerts to minimize casualties.</div></front></TEI><pubmed><MedlineCitation Owner="NLM" Status="MEDLINE"><PMID Version="1">24473550</PMID><DateCreated><Year>2014</Year><Month>01</Month><Day>29</Day></DateCreated><DateCompleted><Year>2014</Year><Month>09</Month><Day>08</Day></DateCompleted><Article PubModel="Print"><Journal><ISSN IssnType="Electronic">1940-9990</ISSN><JournalIssue CitedMedium="Internet"><Volume>7</Volume><Issue>6</Issue><PubDate><Year>2013</Year><Month>Dec</Month></PubDate></JournalIssue><Title>IEEE transactions on biomedical circuits and systems</Title><ISOAbbreviation>IEEE Trans Biomed Circuits Syst</ISOAbbreviation></Journal><ArticleTitle>Leveraging knowledge from physiological data: on-body heat stress risk prediction with sensor networks.</ArticleTitle><Pagination><MedlinePgn>861-70</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1109/TBCAS.2013.2254485</ELocationID><Abstract><AbstractText>The paper demonstrates that wearable sensor systems, coupled with real-time on-body processing and actuation, can enhance safety for wearers of heavy protective equipment who are subjected to harsh thermal environments by reducing risk of Uncompensable Heat Stress (UHS). The work focuses on Explosive Ordnance Disposal operatives and shows that predictions of UHS risk can be performed in real-time with sufficient accuracy for real-world use. Furthermore, it is shown that the required sensory input for such algorithms can be obtained with wearable, non-intrusive sensors. Two algorithms, one based on Bayesian nets and another on decision trees, are presented for determining the heat stress risk, considering the mean skin temperature prediction as a proxy. The algorithms are trained on empirical data and have accuracies of 92.1±2.9% and 94.4±2.1%, respectively when tested using leave-one-subject-out cross-validation. In applications such as Explosive Ordnance Disposal operative monitoring, such prediction algorithms can enable autonomous actuation of cooling systems and haptic alerts to minimize casualties.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Gaura</LastName><ForeName>Elena</ForeName><Initials>E</Initials></Author><Author ValidYN="Y"><LastName>Kemp</LastName><ForeName>John</ForeName><Initials>J</Initials></Author><Author ValidYN="Y"><LastName>Brusey</LastName><ForeName>James</ForeName><Initials>J</Initials></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>IEEE Trans Biomed Circuits Syst</MedlineTA><NlmUniqueID>101312520</NlmUniqueID><ISSNLinking>1932-4545</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName MajorTopicYN="N" UI="D061725">Accelerometry</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D000293">Adolescent</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D000328">Adult</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D000465">Algorithms</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D003020">Clothing</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D020000">Decision Support Systems, Clinical</DescriptorName><QualifierName MajorTopicYN="N" UI="Q000295">instrumentation</QualifierName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D004867">Equipment Design</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D061388">Firefighters</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D018882">Heat Stress Disorders</DescriptorName><QualifierName MajorTopicYN="Y" UI="Q000175">diagnosis</QualifierName><QualifierName MajorTopicYN="N" UI="Q000517">prevention & control</QualifierName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D006801">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D008297">Male</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D015233">Models, Statistical</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D058998">Remote Sensing Technology</DescriptorName><QualifierName MajorTopicYN="Y" UI="Q000295">instrumentation</QualifierName><QualifierName MajorTopicYN="Y" UI="Q000379">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D015203">Reproducibility of Results</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D012881">Skin Temperature</DescriptorName><QualifierName MajorTopicYN="N" UI="Q000502">physiology</QualifierName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D055815">Young Adult</DescriptorName></MeshHeading></MeshHeadingList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="entrez"><Year>2014</Year><Month>1</Month><Day>30</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2014</Year><Month>1</Month><Day>30</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2014</Year><Month>9</Month><Day>10</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="doi">10.1109/TBCAS.2013.2254485</ArticleId><ArticleId IdType="pubmed">24473550</ArticleId></ArticleIdList></PubmedData></pubmed></record>Pour manipuler ce document sous Unix (Dilib)
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