Using frequency analysis to improve the precision of human body posture algorithms based on Kalman filters.
Identifieur interne : 000008 ( Main/Exploration ); précédent : 000007; suivant : 000009Using frequency analysis to improve the precision of human body posture algorithms based on Kalman filters.
Auteurs : Alberto Olivares [Espagne] ; J M G Rriz [Espagne] ; J. Ramírez [Espagne] ; G. Olivares [Espagne]Source :
- Computers in biology and medicine [ 1879-0534 ] ; 2016.
Abstract
With the advent of miniaturized inertial sensors many systems have been developed within the last decade to study and analyze human motion and posture, specially in the medical field. Data measured by the sensors are usually processed by algorithms based on Kalman Filters in order to estimate the orientation of the body parts under study. These filters traditionally include fixed parameters, such as the process and observation noise variances, whose value has large influence in the overall performance. It has been demonstrated that the optimal value of these parameters differs considerably for different motion intensities. Therefore, in this work, we show that, by applying frequency analysis to determine motion intensity, and varying the formerly fixed parameters accordingly, the overall precision of orientation estimation algorithms can be improved, therefore providing physicians with reliable objective data they can use in their daily practice.
DOI: 10.1016/j.compbiomed.2015.08.007
PubMed: 26337122
Affiliations:
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Olivares</name><affiliation wicri:level="4"><nlm:affiliation>Department of Computer Architecture and Computer Technology, University of Granada, Spain; Research Centre for Information and Communications Technologies of the University of Granada (CITIC-UGR), Spain.</nlm:affiliation><country xml:lang="fr">Espagne</country><wicri:regionArea>Department of Computer Architecture and Computer Technology, University of Granada, Spain; Research Centre for Information and Communications Technologies of the University of Granada (CITIC-UGR)</wicri:regionArea><orgName type="university">Université de Grenade</orgName><placeName><settlement type="city">Grenade (Espagne)</settlement><region nuts="2" type="region">Andalousie</region></placeName></affiliation></author></analytic><series><title level="j">Computers in biology and medicine</title><idno type="eISSN">1879-0534</idno><imprint><date when="2016" type="published">2016</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">With the advent of miniaturized inertial sensors many systems have been developed within the last decade to study and analyze human motion and posture, specially in the medical field. Data measured by the sensors are usually processed by algorithms based on Kalman Filters in order to estimate the orientation of the body parts under study. These filters traditionally include fixed parameters, such as the process and observation noise variances, whose value has large influence in the overall performance. It has been demonstrated that the optimal value of these parameters differs considerably for different motion intensities. 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Olivares</name><name sortKey="Ramirez, J" sort="Ramirez, J" uniqKey="Ramirez J" first="J" last="Ramírez">J. Ramírez</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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