Abstract:
Redirected walking enables immersive walking experience in a limited-sized room. To apply redirected walking efficiently and minimize the number of resets, an accurate pa...Show MoreMetadata
Abstract:
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.
Date of Conference: 18-22 March 2018
Date Added to IEEE Xplore: 30 August 2018
ISBN Information: