Clinically deployable Kinesia™ technology for automated tremor assessment
Identifieur interne : 002396 ( Main/Exploration ); précédent : 002395; suivant : 002397Clinically deployable Kinesia™ technology for automated tremor assessment
Auteurs : Joseph P. Giuffrida [États-Unis] ; David E. Riley [États-Unis] ; Brian N. Maddux [États-Unis] ; Dustin A. Heldman [États-Unis]Source :
- Movement Disorders [ 0885-3185 ] ; 2009-04-15.
Descripteurs français
- Pascal (Inist)
- Wicri :
- topic : Technologie.
English descriptors
- KwdEn :
- MESH :
- complications : Parkinson Disease.
- diagnosis : Tremor.
- etiology : Tremor.
- methods : Automatic Data Processing.
- Algorithms, Humans, Kinesics, Severity of Illness Index.
Abstract
The objective was to design, build, and assess Kinesia™, a wireless system for automated assessment of Parkinson's disease (PD) tremor. The current standard in evaluating PD is the Unified Parkinson's Disease Rating Scale (UPDRS), a qualitative ranking system typically completed during an office visit. Kinesia integrates accelerometers and gyroscopes in a compact patient‐worn unit to capture kinematic movement disorder features. Objectively quantifying PD manifestations with increased time resolution should aid in evaluating efficacy of treatment protocols and improve patient management. In this study, PD subjects performed the tremor subset of the UPDRS motor section while wearing Kinesia. Quantitative kinematic features were processed and highly correlated to clinician scores for rest tremor (r2 = 0.89), postural tremor (r2 = 0.90), and kinetic tremor (r2 = 0.69). The quantitative features were used to develop a mathematical model that predicted tremor severity scores for new data with low errors. Finally, PD subjects indicated high clinical acceptance. © 2009 Movement Disorder Society
Url:
DOI: 10.1002/mds.22445
Affiliations:
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Disord.</title><idno type="ISSN">0885-3185</idno><idno type="eISSN">1531-8257</idno><imprint><publisher>Wiley Subscription Services, Inc., A Wiley Company</publisher><pubPlace>Hoboken</pubPlace><date type="published" when="2009-04-15">2009-04-15</date><biblScope unit="vol">24</biblScope><biblScope unit="issue">5</biblScope><biblScope unit="page" from="723">723</biblScope><biblScope unit="page" to="730">730</biblScope></imprint><idno type="ISSN">0885-3185</idno></series><idno type="istex">5E3ECD49515AA95782467635A7EEFFE83BEF1245</idno><idno type="DOI">10.1002/mds.22445</idno><idno type="ArticleID">MDS22445</idno></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">0885-3185</idno></seriesStmt></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Algorithms</term><term>Automatic Data Processing (methods)</term><term>Humans</term><term>Kinesia</term><term>Kinesics</term><term>Nervous system diseases</term><term>Parkinson Disease (complications)</term><term>Parkinson disease</term><term>Parkinson's disease</term><term>Severity of Illness Index</term><term>Technology</term><term>Tremor</term><term>Tremor (diagnosis)</term><term>Tremor (etiology)</term><term>UPDRS</term><term>tremor</term></keywords><keywords scheme="MESH" qualifier="complications" xml:lang="en"><term>Parkinson Disease</term></keywords><keywords scheme="MESH" qualifier="diagnosis" xml:lang="en"><term>Tremor</term></keywords><keywords scheme="MESH" qualifier="etiology" xml:lang="en"><term>Tremor</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Automatic Data Processing</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Algorithms</term><term>Humans</term><term>Kinesics</term><term>Severity of Illness Index</term></keywords><keywords scheme="Pascal" xml:lang="fr"><term>Maladie de Parkinson</term><term>Pathologie du système nerveux</term><term>Technologie</term><term>Tremblement</term></keywords><keywords scheme="Wicri" type="topic" xml:lang="fr"><term>Technologie</term></keywords></textClass><langUsage><language ident="en">en</language></langUsage></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">The objective was to design, build, and assess Kinesia™, a wireless system for automated assessment of Parkinson's disease (PD) tremor. The current standard in evaluating PD is the Unified Parkinson's Disease Rating Scale (UPDRS), a qualitative ranking system typically completed during an office visit. Kinesia integrates accelerometers and gyroscopes in a compact patient‐worn unit to capture kinematic movement disorder features. Objectively quantifying PD manifestations with increased time resolution should aid in evaluating efficacy of treatment protocols and improve patient management. In this study, PD subjects performed the tremor subset of the UPDRS motor section while wearing Kinesia. Quantitative kinematic features were processed and highly correlated to clinician scores for rest tremor (r2 = 0.89), postural tremor (r2 = 0.90), and kinetic tremor (r2 = 0.69). The quantitative features were used to develop a mathematical model that predicted tremor severity scores for new data with low errors. Finally, PD subjects indicated high clinical acceptance. © 2009 Movement Disorder Society</div></front></TEI><affiliations><list><country><li>États-Unis</li></country><region><li>Ohio</li></region></list><tree><country name="États-Unis"><region name="Ohio"><name sortKey="Giuffrida, Joseph P" sort="Giuffrida, Joseph P" uniqKey="Giuffrida J" first="Joseph P." last="Giuffrida">Joseph P. Giuffrida</name></region><name sortKey="Heldman, Dustin A" sort="Heldman, Dustin A" uniqKey="Heldman D" first="Dustin A." last="Heldman">Dustin A. Heldman</name><name sortKey="Maddux, Brian N" sort="Maddux, Brian N" uniqKey="Maddux B" first="Brian N." last="Maddux">Brian N. Maddux</name><name sortKey="Riley, David E" sort="Riley, David E" uniqKey="Riley D" first="David E." last="Riley">David E. Riley</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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