Real-Time Gait Reconstruction for Virtual Reality Using a Single Sensor

Feigl T, Gruner L, Mutschler C, Roth D (2020)

Publication Language: English

Publication Type: Conference contribution, Conference Contribution

Publication year: 2020

Publisher: Institute of Electrical and Electronics Engineers Inc.

Series: IEEE Proc. International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct)

Pages Range: 84-89

Conference Proceedings Title: Adjunct Proceedings of the 2020 IEEE International Symposium on Mixed and Augmented Reality, ISMAR-Adjunct 2020

Event location: Ipojuca, Pernambuco BR

ISBN: 9781728176758

DOI: 10.1109/ISMAR-Adjunct51615.2020.00037

Abstract

Embodying users through avatars based on motion tracking and reconstruction is an ongoing challenge for VR application developers. High quality VR systems use full-body tracking or inverse kinematics to reconstruct the motion of the lower extremities and control the avatar animation. Mobile systems are limited to the motion sensing of head-mounted displays (HMDs) and typically cannot offer this.We propose an approach to reconstruct gait motions from a single head-mounted accelerometer. We train our models to map head motions to corresponding ground truth gait phases. To reconstruct leg motion, the models predict gait phases to trigger equivalent synthetic animations. We designed four models: A threshold-based, a correlation-based, a Support Vector Machine (SVM)-based and a bidirectional long-Term short-Term memory (BLSTM)-based model. Our experiments show that, while the BLSTM approach is the most accurate, only the correlation approach runs on a mobile VR system in real time with sufficient accuracy. Our user study with 21 test subjects examined the effects of our approach on simulator sickness and showed significantly less negative effects on disorientation.

Authors with CRIS profile

Tobias Feigl Lehrstuhl für Informatik 2 (Programmiersysteme) Christopher Mutschler
Daniel Roth Juniorprofessur für Human-Centered Computing and Extended Reality

Related research project(s)

Recurrent Neuronal Networks (RNNs) for Real-Time Estimation of Nonlinear Motion Models (RuNN) Oct. 1, 2017 - March 31, 2021

Involved external institutions

Fraunhofer-Institut für Integrierte Schaltungen (IIS) DE Germany (DE)

How to cite

APA:

Feigl, T., Gruner, L., Mutschler, C., & Roth, D. (2020). Real-Time Gait Reconstruction for Virtual Reality Using a Single Sensor. In Adjunct Proceedings of the 2020 IEEE International Symposium on Mixed and Augmented Reality, ISMAR-Adjunct 2020 (pp. 84-89). Ipojuca, Pernambuco, BR: Institute of Electrical and Electronics Engineers Inc..

MLA:

Feigl, Tobias, et al. "Real-Time Gait Reconstruction for Virtual Reality Using a Single Sensor." Proceedings of the 2020 IEEE International Symposium on Mixed and Augmented Reality, ISMAR-Adjunct 2020, Ipojuca, Pernambuco Institute of Electrical and Electronics Engineers Inc., 2020. 84-89.

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