Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/61387
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dc.contributor.authorAmira Mohamed Tamiri
dc.date.accessioned2014-06-09T09:03:33Z
dc.date.available2014-06-09T09:03:33Z
dc.date.copyright2014en_US
dc.date.issued2014
dc.identifier.urihttp://hdl.handle.net/10356/61387
dc.description.abstractWith the world’s population becoming an aging population, the incidence of stroke is increasing. The need to automate the rehabilitation process of stroke grows. With motion analysis of the movements of stroke patients, it is also possible for a development of stroke assessment scales using instrumentation like sensors. This report analyses the upper extremity motion of stroke patients executing five Activities of Daily Living tasks. Using programs like Python and Matlab, measurements of the motions were analysed to determine the Range of Motion (ADL), Peak Velocity and Completion Time of all patients along with healthy subjects, for comparison. With these, an instrumentation assessment scale would be able to determine the degree of disability of a stroke patient. However, these findings can be further developed with determining linearity of motion and jerk score movement so that an accurate instrumentation scale can be developed.en_US
dc.format.extent65 p.en_US
dc.language.isoenen_US
dc.rightsNanyang Technological University
dc.subjectDRNTU::Engineering::Mechanical engineering::Assistive technologyen_US
dc.titleData analysis and study of human motions in activity of daily living for stroke rehabilitationen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorChen I-Mingen_US
dc.contributor.schoolSchool of Mechanical and Aerospace Engineeringen_US
dc.description.degreeBachelor of Engineering (Mechanical Engineering)en_US
dc.contributor.researchRobotics Research Centreen_US
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Appears in Collections:MAE Student Reports (FYP/IA/PA/PI)
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