Redirected walking enables immersive walking experience in a limited-sized room. To apply redirected walking efficiently and minimize the number of resets, an accurate path prediction algorithm is required. We propose a data-driven path prediction model using Long Short-Term Memory(LSTM) network. User path data was collected via path exploration experiment on a maze-like environment and fed into LSTM network. Our algorithm can predict user's future path based on user's past position and facing direction data. We compare our path prediction result with actual user data and show that our model can accurately predict user's future path.
|Title of host publication||25th IEEE Conference on Virtual Reality and 3D User Interfaces, VR 2018 - Proceedings|
|Editors||Frank Steinicke, Bruce Thomas, Kiyoshi Kiyokawa, Greg Welch|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||2|
|Publication status||Published - 2018 Aug 24|
|Event||25th IEEE Conference on Virtual Reality and 3D User Interfaces, VR 2018 - Reutlingen, Germany|
Duration: 2018 Mar 18 → 2018 Mar 22
|Name||25th IEEE Conference on Virtual Reality and 3D User Interfaces, VR 2018 - Proceedings|
|Conference||25th IEEE Conference on Virtual Reality and 3D User Interfaces, VR 2018|
|Period||18/3/18 → 18/3/22|
Bibliographical noteFunding Information:
This work was supported by Samsung Research Funding Center of Samsung Electronics under Project Number SRFC-IT1601-04.
© 2018 IEEE.
All Science Journal Classification (ASJC) codes
- Human-Computer Interaction
- Modelling and Simulation
- Media Technology