Stride Length Estimation Using ANN
| aut.embargo | No | en_NZ |
| aut.thirdpc.contains | No | en_NZ |
| dc.contributor.advisor | Huang, Loulin | |
| dc.contributor.author | Liu, Yu | |
| dc.date.accessioned | 2018-10-26T02:59:07Z | |
| dc.date.available | 2018-10-26T02:59:07Z | |
| dc.date.copyright | 2018 | |
| dc.date.issued | 2018 | |
| dc.date.updated | 2018-10-26T01:20:36Z | |
| dc.description.abstract | Measuring the stride length of a pedestrian is an essential task for many applications, such as augmented reality (AR) applications tracking devices and motion monitoring devices. In the traditional position estimation method such as the double integral of the acceleration signal, the signal noise of the acceleration from the Inertial measurement units (IMU) leads to a cumulative error. It is the bottleneck of the positioning perform- ance in most of Inertial Navigation System (INS). Moreover, in many applications, such as AR and tracking devices, sensors are attached to the human body and used in the indoor environment, which is hard to obtain the position of the device through GPS or traditional INS. In this paper, we proposed a novel method that transforms the position information to step length, and then we use Artificial Neural Network (ANN) to estimate the step length through the data from the IMU. The conducted experiments show that the proposed method achieved less than 2% error in a distance of 62.3m. Comparing to the traditional double integral method, it has superior performance and better ability to handle the signal noise from a low-cost IMU. | en_NZ |
| dc.identifier.uri | https://hdl.handle.net/10292/11907 | |
| dc.language.iso | en | en_NZ |
| dc.publisher | Auckland University of Technology | |
| dc.rights.accessrights | OpenAccess | |
| dc.subject | Neural network | en_NZ |
| dc.subject | IMU | en_NZ |
| dc.subject | Stride length estimation | en_NZ |
| dc.subject | Inertial navigation | en_NZ |
| dc.title | Stride Length Estimation Using ANN | en_NZ |
| dc.type | Thesis | en_NZ |
| thesis.degree.grantor | Auckland University of Technology | |
| thesis.degree.level | Masters Theses | |
| thesis.degree.name | Master of Computer and Information Sciences | en_NZ |