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dc.contributor.advisor O'Malley, Marcia K.
dc.creatorPurkayastha, Sagar
dc.date.accessioned 2013-09-16T16:09:32Z
dc.date.accessioned 2013-09-16T16:10:41Z
dc.date.available 2013-09-16T16:09:32Z
dc.date.available 2013-09-16T16:10:41Z
dc.date.created 2013-05
dc.date.issued 2013-09-16
dc.date.submitted May 2013
dc.identifier.urihttp://hdl.handle.net/1911/72025
dc.description.abstract This thesis identifies and analyzes successful movement strategies for the completion of a complex dynamic task. In the past it has been shown that movement strategies correlate well to performance for simple tasks. Therefore, in this thesis I was motivated to find out if motion based metrics correlated well to performance for more complicated motor tasks. First, the Nintendo Wiimote was verified as a suitable gaming interface enabling gross human motion capture through experimental comparisons with other gaming interfaces and precision sensors. Then, a complex motor task was rendered in an open-source gaming environment. This environment enabled the design of a rhythmic task that could be controlled with the Wiimote while data were simultaneously recorded for later analysis. For the task, success and failure could be explained by high correlation between two motion based performance metrics, mean absolute jerk (MAJ) and average frequency (AVF) per trial. A logistic regression analysis revealed that each subject had a range of MAJ and AVF values for being successful, outside of which they were unsuccessful. Therefore, this thesis identifies motion based performance metrics for a novel motor control task that is significantly difficult to master and the techniques used to identify successful movement strategies can be used for predicting success for other such complex dynamic tasks.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.subjectGaming controllers
Motion capture
Complex dynamic task
Prediction
Movement strategies
dc.title Analysis of human movement for a complex dynamic task: What predicts success?
dc.contributor.committeeMember Byrne, Michael D.
dc.contributor.committeeMember Dick, Andrew J.
dc.date.updated 2013-09-16T16:10:41Z
dc.identifier.slug 123456789/ETD-2013-05-344
dc.type.genre Thesis
dc.type.material Text
thesis.degree.department Mechanical Engineering and Materials Science
thesis.degree.discipline Engineering
thesis.degree.grantor Rice University
thesis.degree.level Masters
thesis.degree.name Master of Science
dc.identifier.citation Purkayastha, Sagar. "Analysis of human movement for a complex dynamic task: What predicts success?." (2013) Master’s Thesis, Rice University. http://hdl.handle.net/1911/72025.


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