Data-Driven Reconstruction of Human Locomotion Using a Single Smartphone
| dc.contributor.author | Eom, Haegwang | en_US |
| dc.contributor.author | Choi, Byungkuk | en_US |
| dc.contributor.author | Noh, Junyong | en_US |
| dc.contributor.editor | J. Keyser, Y. J. Kim, and P. Wonka | en_US |
| dc.date.accessioned | 2015-03-03T12:50:52Z | |
| dc.date.available | 2015-03-03T12:50:52Z | |
| dc.date.issued | 2014 | en_US |
| dc.description.abstract | Generating a visually appealing human motion sequence using low-dimensional control signals is a major line of study in the motion research area in computer graphics. We propose a novel approach that allows us to reconstruct full body human locomotion using a single inertial sensing device, a smartphone. Smartphones are among the most widely used devices and incorporate inertial sensors such as an accelerometer and a gyroscope. To find a mapping between a full body pose and smartphone sensor data, we perform low dimensional embedding of full body motion capture data, based on a Gaussian Process Latent Variable Model. Our system ensures temporal coherence between the reconstructed poses by using a state decomposition model for automatic phase segmentation. Finally, application of the proposed nonlinear regression algorithm finds a proper mapping between the latent space and the sensor data. Our framework effectively reconstructs plausible 3D locomotion sequences. We compare the generated animation to ground truth data obtained using a commercial motion capture system. | en_US |
| dc.description.seriesinformation | Computer Graphics Forum | en_US |
| dc.identifier.issn | 1467-8659 | en_US |
| dc.identifier.uri | https://doi.org/10.1111/cgf.12469 | en_US |
| dc.publisher | The Eurographics Association and John Wiley and Sons Ltd. | en_US |
| dc.title | Data-Driven Reconstruction of Human Locomotion Using a Single Smartphone | en_US |