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A novel bio-kinematic encoder for human exercise representation and decomposition - part 2: robustness and optimisation

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dc.contributor.authorLi, S.
dc.contributor.authorCaelli, T.
dc.contributor.authorFerraro, M.
dc.contributor.authorPathirana, P.N.
dc.contributor.conference2013 International Conference on Control, Automation and Information Sciences (ICCAIS) (25 Nov 2013 - 28 Nov 2013 : Nha Trang City, Vietnam)
dc.date.issued2014
dc.description.abstractBio-kinematic characterisations of human exercises constitute dealing with parameters such as velocity, acceleration, joint angles, etc. A majority of these are measured directly from various sensors ranging from RGB cameras to inertial sensors. However, due to certain limitations associated with these sensors, such as inherent noise, filters are required to be implemented to subjugate the effect from the noise. When the two-component (trajectory shape and dynamics) bio-kinematic encoding model is being established to represent an exercise, reducing the effect from noise embedded in raw data will be important since the underlying model can be quite sensitive to noise. In this paper, we examine and compare some commonly used filters, namely least-square Gaussian filter, Savitzky-Golay filter and optimal Kalman filter, with four groups of real data collected from Microsoft Kinect©, and assert that Savitzky-Golay filter is the best one when establishing an underlying model for human exercise representation.
dc.identifier.citation2013 International Conference on Control, Automation and Information Sciences, ICCAIS 2013, 2014, pp.30-35
dc.identifier.doi10.1109/ICCAIS.2013.6720525
dc.identifier.isbn9781479905690
dc.identifier.urihttps://hdl.handle.net/11541.2/137214
dc.language.isoen
dc.publisherIEEE
dc.publisher.placeUS
dc.rightsCopyright 2013 IEEE
dc.source.urihttps://doi.org/10.1109/ICCAIS.2013.6720525
dc.subjectnoise
dc.subjecttrajectory
dc.subjectkalman filters
dc.subjectsensors
dc.subjectencoding
dc.subjectshape
dc.subjectnoise measurement
dc.titleA novel bio-kinematic encoder for human exercise representation and decomposition - part 2: robustness and optimisation
dc.typeConference paper
pubs.publication-statusPublished
ror.mmsid9916284677001831

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