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dc.contributor.advisorTeng, Danielen_US
dc.contributor.advisorDinh, Anhen_US
dc.contributor.advisorBasran, Jennyen_US
dc.creatorXia, Xiaoyeen_US
dc.date.accessioned2013-02-26T12:00:23Z
dc.date.available2013-02-26T12:00:23Z
dc.date.created2012-01en_US
dc.date.issued2013-02-25en_US
dc.date.submittedJanuary 2012en_US
dc.identifier.urihttp://hdl.handle.net/10388/ETD-2012-01-311en_US
dc.description.abstractNear-fall detection is important for medical research since it can help doctors diagnose fall-related diseases and also help alert both doctors and patients of possible falls. However, in people’s daily life, there are lots of similarities between near-falls and other Activities of Daily Living (ADLs), which makes near-falls particularly difficult to detect. In order to find the subtle difference between ADLs and near-fall and accurately identify the latter, the movement of whole human body needs to be captured and displayed by a computer generated avatar. In this thesis, a wireless inertial motion capture system consisting of a central control host and ten sensor nodes is used to capture human body movements. Each of the ten sensor nodes in the system has a tri-axis accelerometer and a tri-axis gyroscope. They are attached to separate locations of a human body to record both angular and acceleration data with which body movements can be captured by applying Euler angle based algorithms, specifically, single rotation order algorithm and the optimal rotation order algorithm. According to the experiment results of capturing ten ADLs, both the single rotation order algorithm and the optimal rotation order algorithm can track normal human body movements without significantly distortion and the latter shows higher accuracy and lower data shifting. Compared to previous inertial systems with magnetometers, this system reduces hardware complexity and software computation while ensures a reasonable accuracy in capturing human body movements.en_US
dc.language.isoengen_US
dc.subjectMotion captureen_US
dc.subjectActivities of Daily Living (ADLs)en_US
dc.subjectInertial sensorsen_US
dc.subjectEuler anglesen_US
dc.titleBody Motion Capture Using Multiple Inertial Sensorsen_US
thesis.degree.departmentElectrical and Computer Engineeringen_US
thesis.degree.disciplineElectrical Engineeringen_US
thesis.degree.grantorUniversity of Saskatchewanen_US
thesis.degree.levelMastersen_US
thesis.degree.nameMaster of Science (M.Sc.)en_US
dc.type.materialtexten_US
dc.type.genreThesisen_US
dc.contributor.committeeMemberKo, Seokbumen_US
dc.contributor.committeeMemberDeters, Ralphen_US
dc.contributor.committeeMemberWahid, Khan A.en_US


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