Utilization of smartphone sensor data for driving state classification
Numerous 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, idli...
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sg-ntu-dr.10356-665302023-03-03T20:42:31Z Utilization of smartphone sensor data for driving state classification Zheng, Shoubi Ho Shen-Shyang School of Computer Engineering DRNTU::Engineering Numerous 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. Bachelor of Engineering (Computer Science) 2016-04-15T03:31:07Z 2016-04-15T03:31:07Z 2016 Final Year Project (FYP) http://hdl.handle.net/10356/66530 en Nanyang Technological University 57 p. application/pdf |
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Singapore Singapore |
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English |
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DRNTU::Engineering |
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DRNTU::Engineering Zheng, Shoubi Utilization of smartphone sensor data for driving state classification |
| description |
Numerous 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. |
| author2 |
Ho Shen-Shyang |
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Ho Shen-Shyang Zheng, Shoubi |
| format |
Final Year Project |
| author |
Zheng, Shoubi |
| author_sort |
Zheng, Shoubi |
| title |
Utilization of smartphone sensor data for driving state classification |
| title_short |
Utilization of smartphone sensor data for driving state classification |
| title_full |
Utilization of smartphone sensor data for driving state classification |
| title_fullStr |
Utilization of smartphone sensor data for driving state classification |
| title_full_unstemmed |
Utilization of smartphone sensor data for driving state classification |
| title_sort |
utilization of smartphone sensor data for driving state classification |
| publishDate |
2016 |
| url |
http://hdl.handle.net/10356/66530 |
| _version_ |
1759854141214031872 |