dc.contributor.authorZheng, Shoubi
dc.date.accessioned2016-04-15T03:31:07Z
dc.date.available2016-04-15T03:31:07Z
dc.date.issued2016
dc.identifier.urihttp://hdl.handle.net/10356/66530
dc.description.abstractNumerous experiments were carried out using a car driving into a multi-storey carpark attached to a shopping mall. The dataset was collected using accelerometer sensor embedded in a smartphone which was placed in the car during the experiment. The collected data can be categorised into driving, idling and walking. The main focus of this project is to identify different motion states occurred in the parking session. Two popular classifiers K-Nearest Neighbour and Support Vector Machine have been evaluated using various parameters to achieve optimal performance. Features were also extracted from the raw dataset to improve classification accuracy.en_US
dc.format.extent57 p.en_US
dc.language.isoenen_US
dc.rightsNanyang Technological University
dc.subjectDRNTU::Engineeringen_US
dc.titleUtilization of smartphone sensor data for driving state classificationen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorHo Shen-Shyangen_US
dc.contributor.schoolSchool of Computer Engineeringen_US
dc.description.degreeBachelor of Engineering (Computer Science)en_US


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