dc.contributor.authorGuo, Kaitong
dc.description.abstractDiabetes diagnosis and condition surveillance are two main challenges in treatment of this disease. One of the methods is to use plantar pressure which will show different pattern according to different disease conditions of diabetes. One problem of this approach is identifying the activity of the patients since different activity may naturally result in various plantar pressures. This dissertation aims at solving this problem by using accelerometer data to recognize human body activities which include walking, jogging, jumping, going upstairs and going downstairs. Beginning with the preliminary data collection work, several approaches to process raw signals and classify different activities from the acceleration data recorded by accelerometers are proposed and tested by this dissertation, where PCA feature extraction method, time and frequency analysis, SVM classifier and RBF Neural Network are involved. Besides, a window segmentation method and the classification strategy based on small windows are introduced. By combining and doing experiments on these methods, PCA feature extraction method and SVM classifier are proven to have the best performances, which can achieve a classification accuracy up to 92%. While the time and frequency domain features, which has been applied by most of the existing works, show poor performances under the experiment environment in this dissertation.en_US
dc.format.extent57 p.en_US
dc.subjectDRNTU::Engineering::Electrical and electronic engineeringen_US
dc.titleAccelerometer based motion activity recognitionen_US
dc.contributor.supervisorGoh Wang Lingen_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeMaster of Science (Computer Control and Automation)en_US

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