| Author | Chanaphan Prasomwong |
| Call Number | AIT Thesis no.IM-10-04 |
| Subject(s) | Accelerometers
|
| Note | A thesis submitted in partial fulfillment of the requirements for the Degree of Master of Engineering in Information Management, School of Engineering and Technology |
| Publisher | Asian Institute of Technology |
| Series Statement | Thesis ; no. IM-10-04 |
| Abstract | Many researchers have studied the problem of classifying patient activities in order to monitor and assist them in their daily life. This study aims to develop a classification model for recognizing users‟ activity with data obtained by the sensors in smart mobile phones. In particular, we focus on the use of the accelerometer, which is a common sensor in new mobile phones such as the iPhone and the Nokia N97. Th e positions and the postures of the mobile phones are captured by the accelerometer, and classified using a Support Vector Machine (SVM) algorithm. Training and test data for seven activities (stationary, standing, running, walking, ascending and descendin g stairs, cycling and driving a car) were collected in with the phone in four postures (head up/face in, head up / face out, head down/ face in, head down/face out ) and six locations on the body, with a classification accuracy of above 92.5% using the SVM a lgorithm. The results provide a solid base for future research in activity classification. |
| Year | 2010 |
| Corresponding Series Added Entry | Asian Institute of Technology. Thesis ; no. IM-10-04 |
| Type | Thesis |
| School | School of Engineering and Technology (SET) |
| Department | Department of Information and Communications Technologies (DICT) |
| Academic Program/FoS | Information Management (IM) |
| Chairperson(s) | Janecek, Paul; |
| Examination Committee(s) | Daqing Zhang;Matthew Dailey; |
| Scholarship Donor(s) | Royal Thai Government (RTG); |
| Degree | Thesis (M. Eng.) - Asian Institute of Technology, 2010 |