Gated Sensor Fusion: A way to Improve the Precision of Ambulatory Human Body Motion Estimation.
Identifieur interne : 000142 ( PubMed/Corpus ); précédent : 000141; suivant : 000143Gated Sensor Fusion: A way to Improve the Precision of Ambulatory Human Body Motion Estimation.
Auteurs : Alberto Olivares ; J M G Rriz ; J. Ramírez ; Gonzalo OlivaresSource :
- Studies in health technology and informatics [ 0926-9630 ] ; 2014.
Abstract
Human body motion is usually variable in terms of intensity and, therefore, any Inertial Measurement Unit attached to a subject will measure both low and high angular rate and accelerations. This can be a problem for the accuracy of orientation estimation algorithms based on adaptive filters such as the Kalman filter, since both the variances of the process noise and the measurement noise are set at the beginning of the algorithm and remain constant during its execution. Setting fixed noise parameters burdens the adaptation capability of the filter if the intensity of the motion changes rapidly. In this work we present a conjoint novel algorithm which uses a motion intensity detector to dynamically vary the noise statistical parameters of different approaches of the Kalman filter. Results show that the precision of the estimated orientation in terms of the RMSE can be improved up to 29% with respect to the standard fixed-parameters approaches.
PubMed: 25488209
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Ramírez</name><affiliation><nlm:affiliation>Dept. of Signal Processing, Telematics and Communications (TSTC), University of Granada, SPAIN.</nlm:affiliation></affiliation></author><author><name sortKey="Olivares, Gonzalo" sort="Olivares, Gonzalo" uniqKey="Olivares G" first="Gonzalo" last="Olivares">Gonzalo Olivares</name><affiliation><nlm:affiliation>Dept. of Computer Architecture and Computer Technology (ATC), University of Granada, SPAIN.</nlm:affiliation></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2014">2014</date><idno type="RBID">pubmed:25488209</idno><idno type="pmid">25488209</idno><idno type="wicri:Area/PubMed/Corpus">000142</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Corpus" wicri:corpus="PubMed">000142</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Gated Sensor Fusion: A way to Improve the Precision of Ambulatory Human Body Motion Estimation.</title><author><name sortKey="Olivares, Alberto" sort="Olivares, Alberto" uniqKey="Olivares A" first="Alberto" last="Olivares">Alberto Olivares</name><affiliation><nlm:affiliation>Dept. of Signal Processing, Telematics and Communications (TSTC), University of Granada, SPAIN.</nlm:affiliation></affiliation></author><author><name sortKey="G Rriz, J M" sort="G Rriz, J M" uniqKey="G Rriz J" first="J M" last="G Rriz">J M G Rriz</name><affiliation><nlm:affiliation>Dept. of Signal Processing, Telematics and Communications (TSTC), University of Granada, SPAIN.</nlm:affiliation></affiliation></author><author><name sortKey="Ramirez, J" sort="Ramirez, J" uniqKey="Ramirez J" first="J" last="Ramírez">J. Ramírez</name><affiliation><nlm:affiliation>Dept. of Signal Processing, Telematics and Communications (TSTC), University of Granada, SPAIN.</nlm:affiliation></affiliation></author><author><name sortKey="Olivares, Gonzalo" sort="Olivares, Gonzalo" uniqKey="Olivares G" first="Gonzalo" last="Olivares">Gonzalo Olivares</name><affiliation><nlm:affiliation>Dept. of Computer Architecture and Computer Technology (ATC), University of Granada, SPAIN.</nlm:affiliation></affiliation></author></analytic><series><title level="j">Studies in health technology and informatics</title><idno type="ISSN">0926-9630</idno><imprint><date when="2014" type="published">2014</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Human body motion is usually variable in terms of intensity and, therefore, any Inertial Measurement Unit attached to a subject will measure both low and high angular rate and accelerations. This can be a problem for the accuracy of orientation estimation algorithms based on adaptive filters such as the Kalman filter, since both the variances of the process noise and the measurement noise are set at the beginning of the algorithm and remain constant during its execution. Setting fixed noise parameters burdens the adaptation capability of the filter if the intensity of the motion changes rapidly. In this work we present a conjoint novel algorithm which uses a motion intensity detector to dynamically vary the noise statistical parameters of different approaches of the Kalman filter. Results show that the precision of the estimated orientation in terms of the RMSE can be improved up to 29% with respect to the standard fixed-parameters approaches.</div></front></TEI><pubmed><MedlineCitation Owner="NLM" Status="In-Data-Review"><PMID Version="1">25488209</PMID><DateCreated><Year>2014</Year><Month>12</Month><Day>09</Day></DateCreated><Article PubModel="Print"><Journal><ISSN IssnType="Print">0926-9630</ISSN><JournalIssue CitedMedium="Internet"><Volume>207</Volume><PubDate><Year>2014</Year></PubDate></JournalIssue><Title>Studies in health technology and informatics</Title><ISOAbbreviation>Stud Health Technol Inform</ISOAbbreviation></Journal><ArticleTitle>Gated Sensor Fusion: A way to Improve the Precision of Ambulatory Human Body Motion Estimation.</ArticleTitle><Pagination><MedlinePgn>37-46</MedlinePgn></Pagination><Abstract><AbstractText>Human body motion is usually variable in terms of intensity and, therefore, any Inertial Measurement Unit attached to a subject will measure both low and high angular rate and accelerations. This can be a problem for the accuracy of orientation estimation algorithms based on adaptive filters such as the Kalman filter, since both the variances of the process noise and the measurement noise are set at the beginning of the algorithm and remain constant during its execution. Setting fixed noise parameters burdens the adaptation capability of the filter if the intensity of the motion changes rapidly. In this work we present a conjoint novel algorithm which uses a motion intensity detector to dynamically vary the noise statistical parameters of different approaches of the Kalman filter. 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