A Bidirectional LSTM for Estimating Dynamic Human Velocities from a Single IMU

Feigl T, Kram S, Woller P, Siddiqui RH, Philippsen M, Mutschler C (2019)

Publication Language: English

Publication Type: Conference contribution, Conference Contribution

Publication year: 2019

Publisher: IEEE

Conference Proceedings Title: Proceedings of the 10th International Conference on Indoor Positioning and Indoor Navigation (IPIN)

Event location: Pisa IT

ISBN: 978-1-7281-1788-1

URI: https://www2.cs.fau.de/publication/download/IPIN2019.pdf

DOI: 10.1109/IPIN.2019.8911814

Abstract

The main challenge in estimating human velocity from noisy Inertial Measurement Units (IMUs) are the errors that accumulate by integrating noisy accelerometer signals over a long time. Known approaches that work on step length estimation are optimized for a specific application, sensor position, and movement type, require an exhaustive (manual) parameter tuning, and can thus not be applied to other movement types or to a broader range of applications. Moreover, varying dynamics (as they are present for instance in sports applications) cause abrupt and unpredictable changes in step frequency or step length and hence result in erroneous velocity estimates.
We use machine learning (ML) and deep learning (DL) to estimate a human’s velocity. Our approach is robust to varying motion states and orientation changes in dynamic situations. On data from a single un-calibrated IMU, our novel recurrent model not only outperforms the state-of-the-art on instantaneous velocity (≤0.10 m/s) and on traveled distance (≤29 m/km). It can also generalize to different and varying rates of motion and provides accurate and precise velocity estimates.

Authors with CRIS profile

Tobias Feigl Lehrstuhl für Informatik 2 (Programmiersysteme) Sebastian Kram
Michael Philippsen Lehrstuhl für Informatik 2 (Programmiersysteme) Christopher Mutschler Lehrstuhl für Informatik 2 (Programmiersysteme)

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., Kram, S., Woller, P., Siddiqui, R.H., Philippsen, M., & Mutschler, C. (2019). A Bidirectional LSTM for Estimating Dynamic Human Velocities from a Single IMU. In IEEE (Eds.), Proceedings of the 10th International Conference on Indoor Positioning and Indoor Navigation (IPIN). Pisa, IT: IEEE.

MLA:

Feigl, Tobias, et al. "A Bidirectional LSTM for Estimating Dynamic Human Velocities from a Single IMU." Proceedings of the 10th International Conference on Indoor Positioning and Indoor Navigation (IPIN), Pisa Ed. IEEE, IEEE, 2019.

BibTeX: Download