Kalman filtering of accelerometer and electromyography data in pathological tremor sensing system
Identifieur interne : 000901 ( Hal/Checkpoint ); précédent : 000900; suivant : 000902Kalman filtering of accelerometer and electromyography data in pathological tremor sensing system
Auteurs : Ferdinand Widjaja [Singapour] ; C. Y. Shee [Singapour] ; W. T. Latt [Singapour] ; W. L. Au [Singapour] ; Philippe Poignet [France] ; Wei Tech Ang [Singapour]Source :
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Abstract
Currently there is a lack of objective clinical diagnosis and classification of tremor is difficult when it is subtle. Thus in previous work, a sensing system has been developed to quantify pathological tremor in human upper limb. In this paper, a Kalman filter algorithm to fuse information from accelerometers and surface electromyography is proposed. As the ground truth, an optical motion tracking system will be utilized. Then two sensor fusion algorithms based on Kalman filter are formulated to estimate the joint angle of the limb from the reading of accelerometers and surface EMG. Initial results using tremor data from two Parkinson's disease patients show promising future in this sensor fusion. The sensing system and the algorithms proposed are useful for actively compensating the tremor and helping the clinicians in tremor diagnostics.
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DOI: 10.1109/ROBOT.2008.4543706
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Hal:lirmm-00196066Le document en format XML
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<front><div type="abstract" xml:lang="en">Currently there is a lack of objective clinical diagnosis and classification of tremor is difficult when it is subtle. Thus in previous work, a sensing system has been developed to quantify pathological tremor in human upper limb. In this paper, a Kalman filter algorithm to fuse information from accelerometers and surface electromyography is proposed. As the ground truth, an optical motion tracking system will be utilized. Then two sensor fusion algorithms based on Kalman filter are formulated to estimate the joint angle of the limb from the reading of accelerometers and surface EMG. Initial results using tremor data from two Parkinson's disease patients show promising future in this sensor fusion. The sensing system and the algorithms proposed are useful for actively compensating the tremor and helping the clinicians in tremor diagnostics.</div>
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<idno type="stamp" n="INRIA-SOPHIA">INRIA Sophia Antipolis - Méditerranée</idno>
<idno type="stamp" n="INRIASO">INRIA-SOPHIA</idno>
<idno type="stamp" n="DEMAR" p="LIRMM">DEambulation et Mouvement ARtificiel</idno>
<idno type="stamp" n="INRIA_TEST">INRIA - Institut National de Recherche en Informatique et en Automatique</idno>
<idno type="stamp" n="INRIA2">INRIA 2</idno>
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<sourceDesc> <biblStruct> <analytic> <title xml:lang="en">Kalman filtering of accelerometer and electromyography data in pathological tremor sensing system</title>
<author role="aut"> <persName> <forename type="first">Ferdinand</forename>
<surname>Widjaja</surname>
</persName>
<idno type="halauthorid">228805</idno>
<affiliation ref="#struct-50935"></affiliation>
</author>
<author role="aut"> <persName> <forename type="first">C.Y.</forename>
<surname>Shee</surname>
</persName>
<idno type="halauthorid">228806</idno>
<affiliation ref="#struct-50935"></affiliation>
</author>
<author role="aut"> <persName> <forename type="first">W. T.</forename>
<surname>Latt</surname>
</persName>
<idno type="halauthorid">1358736</idno>
<affiliation ref="#struct-50935"></affiliation>
</author>
<author role="aut"> <persName> <forename type="first">W.L.</forename>
<surname>Au</surname>
</persName>
<idno type="halauthorid">228807</idno>
<affiliation ref="#struct-50935"></affiliation>
</author>
<author role="aut"> <persName> <forename type="first">Philippe</forename>
<surname>Poignet</surname>
</persName>
<email type="md5">16f59f61a4f301df6eb6c8e69db9b7f8</email>
<email type="domain">lirmm.fr</email>
<idno type="idhal" notation="string">philippe-poignet</idno>
<idno type="idhal" notation="numeric">5304</idno>
<idno type="halauthorid">1048624</idno>
<affiliation ref="#struct-391198"></affiliation>
</author>
<author role="aut"> <persName> <forename type="first">Wei Tech</forename>
<surname>Ang</surname>
</persName>
<idno type="halauthorid">228808</idno>
<affiliation ref="#struct-50935"></affiliation>
</author>
</analytic>
<monogr> <title level="m">IEEE International Conference on Robotics and Automation</title>
<meeting> <title>ICRA: International Conference on Robotics and Automation</title>
<date type="start">2008-05-19</date>
<date type="end">2008-05-23</date>
<settlement>Pasadena, CA</settlement>
<country key="US">United States</country>
</meeting>
<imprint> <biblScope unit="pp">3250-3255</biblScope>
<date type="datePub">2008-05</date>
</imprint>
</monogr>
<idno type="doi">10.1109/ROBOT.2008.4543706</idno>
<ref type="publisher">http://ewh.ieee.org/soc/ras/conf/FullySponsored/ICRA/2008/index.html</ref>
</biblStruct>
</sourceDesc>
<profileDesc> <langUsage> <language ident="en">English</language>
</langUsage>
<textClass> <keywords scheme="author"> <term xml:lang="en">Tremor</term>
</keywords>
<classCode scheme="halDomain" n="spi.auto">Engineering Sciences [physics]/Automatic</classCode>
<classCode scheme="halTypology" n="COMM">Conference papers</classCode>
</textClass>
<abstract xml:lang="en">Currently there is a lack of objective clinical diagnosis and classification of tremor is difficult when it is subtle. Thus in previous work, a sensing system has been developed to quantify pathological tremor in human upper limb. In this paper, a Kalman filter algorithm to fuse information from accelerometers and surface electromyography is proposed. As the ground truth, an optical motion tracking system will be utilized. Then two sensor fusion algorithms based on Kalman filter are formulated to estimate the joint angle of the limb from the reading of accelerometers and surface EMG. Initial results using tremor data from two Parkinson's disease patients show promising future in this sensor fusion. The sensing system and the algorithms proposed are useful for actively compensating the tremor and helping the clinicians in tremor diagnostics.</abstract>
</profileDesc>
</hal>
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