An automated approach to measuring child movement and location in the early childhood classroom.
Identifieur interne : 000886 ( Main/Exploration ); précédent : 000885; suivant : 000887An automated approach to measuring child movement and location in the early childhood classroom.
Auteurs : Dwight W. Irvin [États-Unis] ; Stephen A. Crutchfield [États-Unis] ; Charles R. Greenwood [États-Unis] ; William D. Kearns [États-Unis] ; Jay Buzhardt [États-Unis]Source :
- Behavior research methods [ 1554-3528 ] ; 2018.
Descripteurs français
- KwdFr :
- Bases de données factuelles (MeSH), Calibrage (MeSH), Développement de l'enfant (MeSH), Enfant d'âge préscolaire (MeSH), Humains (MeSH), Mouvement (MeSH), Mâle (MeSH), Normes de référence (MeSH), Projets pilotes (MeSH), Technologie de télédétection (instrumentation), Établissements scolaires (MeSH).
- MESH :
English descriptors
- KwdEn :
- MESH :
- instrumentation : Remote Sensing Technology.
- Calibration, Child Development, Child, Preschool, Databases, Factual, Humans, Male, Movement, Pilot Projects, Reference Standards, Schools.
Abstract
Children's movement is an important issue in child development and outcome in early childhood research, intervention, and practice. Digital sensor technologies offer improvements in naturalistic movement measurement and analysis. We conducted validity and feasibility testing of a real-time, indoor mapping and location system (Ubisense, Inc.) within a preschool classroom. Real-time indoor mapping has several implications with respect to efficiently and conveniently: (a) determining the activity areas where children are spending the most and least time per day (e.g., music); and (b) mapping a focal child's atypical real-time movements (e.g., lapping behavior). We calibrated the accuracy of Ubisense point-by-point location estimates (i.e., X and Y coordinates) against laser rangefinder measurements using several stationary points and atypical movement patterns as reference standards. Our results indicate that activity areas occupied and atypical movement patterns could be plotted with an accuracy of 30.48 cm (1 ft) using a Ubisense transponder tag attached to the participating child's shirt. The accuracy parallels findings of other researchers employing Ubisense to study atypical movement patterns in individuals at risk for dementia in an assisted living facility. The feasibility of Ubisense was tested in an approximately 90-min assessment of two children, one typically developing and one with Down syndrome, during natural classroom activities, and the results proved positive. Implications for employing Ubisense in early childhood classrooms as a data-based decision-making tool to support children's development and its potential integration with other wearable sensor technologies are discussed.
DOI: 10.3758/s13428-017-0912-8
PubMed: 28597234
Affiliations:
- États-Unis
- Floride, Kansas
- Lawrence (Kansas), Tampa
- Université de Floride du Sud, Université du Kansas
Links toward previous steps (curation, corpus...)
Le document en format XML
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Greenwood</name><affiliation wicri:level="4"><nlm:affiliation>Juniper Gardens Children's Project, University of Kansas, 444 Minnesota Avenue Ste. 300, Kansas City, KS, 66101, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Juniper Gardens Children's Project, University of Kansas, 444 Minnesota Avenue Ste. 300, Kansas City, KS, 66101</wicri:regionArea><orgName type="university">Université du Kansas</orgName><placeName><settlement type="city">Lawrence (Kansas)</settlement><region type="state">Kansas</region></placeName></affiliation></author><author><name sortKey="Kearns, William D" sort="Kearns, William D" uniqKey="Kearns W" first="William D" last="Kearns">William D. Kearns</name><affiliation wicri:level="4"><nlm:affiliation>Department of Rehabilitation and Mental Health Counseling, University of South Florida, 13301 N. Bruce B. Downs Blvd., Tampa, FL, 33620, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Rehabilitation and Mental Health Counseling, University of South Florida, 13301 N. Bruce B. 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Digital sensor technologies offer improvements in naturalistic movement measurement and analysis. We conducted validity and feasibility testing of a real-time, indoor mapping and location system (Ubisense, Inc.) within a preschool classroom. Real-time indoor mapping has several implications with respect to efficiently and conveniently: (a) determining the activity areas where children are spending the most and least time per day (e.g., music); and (b) mapping a focal child's atypical real-time movements (e.g., lapping behavior). We calibrated the accuracy of Ubisense point-by-point location estimates (i.e., X and Y coordinates) against laser rangefinder measurements using several stationary points and atypical movement patterns as reference standards. Our results indicate that activity areas occupied and atypical movement patterns could be plotted with an accuracy of 30.48 cm (1 ft) using a Ubisense transponder tag attached to the participating child's shirt. The accuracy parallels findings of other researchers employing Ubisense to study atypical movement patterns in individuals at risk for dementia in an assisted living facility. The feasibility of Ubisense was tested in an approximately 90-min assessment of two children, one typically developing and one with Down syndrome, during natural classroom activities, and the results proved positive. Implications for employing Ubisense in early childhood classrooms as a data-based decision-making tool to support children's development and its potential integration with other wearable sensor technologies are discussed.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" IndexingMethod="Curated" Owner="NLM"><PMID Version="1">28597234</PMID><DateCompleted><Year>2019</Year><Month>02</Month><Day>18</Day></DateCompleted><DateRevised><Year>2019</Year><Month>02</Month><Day>19</Day></DateRevised><Article PubModel="Print"><Journal><ISSN IssnType="Electronic">1554-3528</ISSN><JournalIssue CitedMedium="Internet"><Volume>50</Volume><Issue>3</Issue><PubDate><Year>2018</Year><Month>06</Month></PubDate></JournalIssue><Title>Behavior research methods</Title><ISOAbbreviation>Behav Res Methods</ISOAbbreviation></Journal><ArticleTitle>An automated approach to measuring child movement and location in the early childhood classroom.</ArticleTitle><Pagination><MedlinePgn>890-901</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.3758/s13428-017-0912-8</ELocationID><Abstract><AbstractText>Children's movement is an important issue in child development and outcome in early childhood research, intervention, and practice. Digital sensor technologies offer improvements in naturalistic movement measurement and analysis. We conducted validity and feasibility testing of a real-time, indoor mapping and location system (Ubisense, Inc.) within a preschool classroom. Real-time indoor mapping has several implications with respect to efficiently and conveniently: (a) determining the activity areas where children are spending the most and least time per day (e.g., music); and (b) mapping a focal child's atypical real-time movements (e.g., lapping behavior). We calibrated the accuracy of Ubisense point-by-point location estimates (i.e., X and Y coordinates) against laser rangefinder measurements using several stationary points and atypical movement patterns as reference standards. Our results indicate that activity areas occupied and atypical movement patterns could be plotted with an accuracy of 30.48 cm (1 ft) using a Ubisense transponder tag attached to the participating child's shirt. The accuracy parallels findings of other researchers employing Ubisense to study atypical movement patterns in individuals at risk for dementia in an assisted living facility. The feasibility of Ubisense was tested in an approximately 90-min assessment of two children, one typically developing and one with Down syndrome, during natural classroom activities, and the results proved positive. Implications for employing Ubisense in early childhood classrooms as a data-based decision-making tool to support children's development and its potential integration with other wearable sensor technologies are discussed.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Irvin</LastName><ForeName>Dwight W</ForeName><Initials>DW</Initials><AffiliationInfo><Affiliation>Juniper Gardens Children's Project, University of Kansas, 444 Minnesota Avenue Ste. 300, Kansas City, KS, 66101, USA. dwirvin@ku.edu.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Crutchfield</LastName><ForeName>Stephen A</ForeName><Initials>SA</Initials><AffiliationInfo><Affiliation>Department of Special Education, California Polytechnic State University at San Luis Obispo, 1 Grand Ave., San Luis Obispo, CA, 93407, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Greenwood</LastName><ForeName>Charles R</ForeName><Initials>CR</Initials><AffiliationInfo><Affiliation>Juniper Gardens Children's Project, University of Kansas, 444 Minnesota Avenue Ste. 300, Kansas City, KS, 66101, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Kearns</LastName><ForeName>William D</ForeName><Initials>WD</Initials><AffiliationInfo><Affiliation>Department of Rehabilitation and Mental Health Counseling, University of South Florida, 13301 N. Bruce B. Downs Blvd., Tampa, FL, 33620, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Buzhardt</LastName><ForeName>Jay</ForeName><Initials>J</Initials><AffiliationInfo><Affiliation>Juniper Gardens Children's Project, University of Kansas, 444 Minnesota Avenue Ste. 300, Kansas City, KS, 66101, USA.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D013486">Research Support, U.S. Gov't, Non-P.H.S.</PublicationType></PublicationTypeList></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>Behav Res Methods</MedlineTA><NlmUniqueID>101244316</NlmUniqueID><ISSNLinking>1554-351X</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D002138" MajorTopicYN="N">Calibration</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D002657" MajorTopicYN="N">Child Development</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D002675" MajorTopicYN="N">Child, Preschool</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D016208" MajorTopicYN="N">Databases, Factual</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D008297" MajorTopicYN="N">Male</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D009068" MajorTopicYN="Y">Movement</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D010865" MajorTopicYN="N">Pilot Projects</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D012015" MajorTopicYN="N">Reference Standards</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D058998" MajorTopicYN="N">Remote Sensing Technology</DescriptorName><QualifierName UI="Q000295" MajorTopicYN="Y">instrumentation</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D012574" MajorTopicYN="N">Schools</DescriptorName></MeshHeading></MeshHeadingList><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="Y">Atypical movement</Keyword><Keyword MajorTopicYN="Y">Classroom location</Keyword><Keyword MajorTopicYN="Y">Data-based decision making</Keyword><Keyword MajorTopicYN="Y">Real-time location system</Keyword><Keyword MajorTopicYN="Y">Ubisense</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="pubmed"><Year>2017</Year><Month>6</Month><Day>10</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2019</Year><Month>2</Month><Day>20</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2017</Year><Month>6</Month><Day>10</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">28597234</ArticleId><ArticleId IdType="doi">10.3758/s13428-017-0912-8</ArticleId><ArticleId IdType="pii">10.3758/s13428-017-0912-8</ArticleId></ArticleIdList></PubmedData></pubmed><affiliations><list><country><li>États-Unis</li></country><region><li>Floride</li><li>Kansas</li></region><settlement><li>Lawrence (Kansas)</li><li>Tampa</li></settlement><orgName><li>Université de Floride du Sud</li><li>Université du Kansas</li></orgName></list><tree><country name="États-Unis"><region name="Kansas"><name sortKey="Irvin, Dwight W" sort="Irvin, Dwight W" uniqKey="Irvin D" first="Dwight W" last="Irvin">Dwight W. Irvin</name></region><name sortKey="Buzhardt, Jay" sort="Buzhardt, Jay" uniqKey="Buzhardt J" first="Jay" last="Buzhardt">Jay Buzhardt</name><name sortKey="Crutchfield, Stephen A" sort="Crutchfield, Stephen A" uniqKey="Crutchfield S" first="Stephen A" last="Crutchfield">Stephen A. Crutchfield</name><name sortKey="Greenwood, Charles R" sort="Greenwood, Charles R" uniqKey="Greenwood C" first="Charles R" last="Greenwood">Charles R. Greenwood</name><name sortKey="Kearns, William D" sort="Kearns, William D" uniqKey="Kearns W" first="William D" last="Kearns">William D. Kearns</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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