Path Prediction Using LSTM Network for Redirected Walking

Yang Hun Cha; Dang Yang Lee; In Kwan Lee

doi:10.1109/VR.2018.8446442

Path Prediction Using LSTM Network for Redirected Walking

Yang Hun Cha, Dang Yang Lee, In Kwan Lee

College of Computing

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

11 Citations (Scopus)

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.

Original language	English
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.
Pages	527-528
Number of pages	2
ISBN (Print)	9781538633656
DOIs	https://doi.org/10.1109/VR.2018.8446442
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

Publication series

Name	25th IEEE Conference on Virtual Reality and 3D User Interfaces, VR 2018 - Proceedings

Conference

Conference	25th IEEE Conference on Virtual Reality and 3D User Interfaces, VR 2018
Country/Territory	Germany
City	Reutlingen
Period	18/3/18 → 18/3/22

Bibliographical note

Funding Information:
This work was supported by Samsung Research Funding Center of Samsung Electronics under Project Number SRFC-IT1601-04.

Publisher Copyright:
© 2018 IEEE.

All Science Journal Classification (ASJC) codes

Human-Computer Interaction
Modelling and Simulation
Media Technology

Access to Document

10.1109/VR.2018.8446442

Cite this

Cha, Y. H., Lee, D. Y., & Lee, I. K. (2018). Path Prediction Using LSTM Network for Redirected Walking. In F. Steinicke, B. Thomas, K. Kiyokawa, & G. Welch (Eds.), 25th IEEE Conference on Virtual Reality and 3D User Interfaces, VR 2018 - Proceedings (pp. 527-528). Article 8446442 (25th IEEE Conference on Virtual Reality and 3D User Interfaces, VR 2018 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/VR.2018.8446442

Cha, Yang Hun ; Lee, Dang Yang ; Lee, In Kwan. / Path Prediction Using LSTM Network for Redirected Walking. 25th IEEE Conference on Virtual Reality and 3D User Interfaces, VR 2018 - Proceedings. editor / Frank Steinicke ; Bruce Thomas ; Kiyoshi Kiyokawa ; Greg Welch. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 527-528 (25th IEEE Conference on Virtual Reality and 3D User Interfaces, VR 2018 - Proceedings).

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title = "Path Prediction Using LSTM Network for Redirected Walking",

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.",

author = "Cha, {Yang Hun} and Lee, {Dang Yang} and Lee, {In Kwan}",

note = "Funding Information: This work was supported by Samsung Research Funding Center of Samsung Electronics under Project Number SRFC-IT1601-04. Publisher Copyright: {\textcopyright} 2018 IEEE.; 25th IEEE Conference on Virtual Reality and 3D User Interfaces, VR 2018 ; Conference date: 18-03-2018 Through 22-03-2018",

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doi = "10.1109/VR.2018.8446442",

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series = "25th IEEE Conference on Virtual Reality and 3D User Interfaces, VR 2018 - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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Cha, YH, Lee, DY & Lee, IK 2018, Path Prediction Using LSTM Network for Redirected Walking. in F Steinicke, B Thomas, K Kiyokawa & G Welch (eds), 25th IEEE Conference on Virtual Reality and 3D User Interfaces, VR 2018 - Proceedings., 8446442, 25th IEEE Conference on Virtual Reality and 3D User Interfaces, VR 2018 - Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 527-528, 25th IEEE Conference on Virtual Reality and 3D User Interfaces, VR 2018, Reutlingen, Germany, 18/3/18. https://doi.org/10.1109/VR.2018.8446442

Path Prediction Using LSTM Network for Redirected Walking. / Cha, Yang Hun; Lee, Dang Yang; Lee, In Kwan.
25th IEEE Conference on Virtual Reality and 3D User Interfaces, VR 2018 - Proceedings. ed. / Frank Steinicke; Bruce Thomas; Kiyoshi Kiyokawa; Greg Welch. Institute of Electrical and Electronics Engineers Inc., 2018. p. 527-528 8446442 (25th IEEE Conference on Virtual Reality and 3D User Interfaces, VR 2018 - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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N2 - 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.

AB - 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.

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Cha YH, Lee DY, Lee IK. Path Prediction Using LSTM Network for Redirected Walking. In Steinicke F, Thomas B, Kiyokawa K, Welch G, editors, 25th IEEE Conference on Virtual Reality and 3D User Interfaces, VR 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2018. p. 527-528. 8446442. (25th IEEE Conference on Virtual Reality and 3D User Interfaces, VR 2018 - Proceedings). doi: 10.1109/VR.2018.8446442

Path Prediction Using LSTM Network for Redirected Walking

Abstract

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Bibliographical note

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