Intention Detection of Gait Adaptation in Natural Settings

Domingos, I., Yang, G.-Z. and Deligianni, F. (2022) Intention Detection of Gait Adaptation in Natural Settings. In: 2021 IEEE Symposium Series on Computational Intelligence (SSCI), Orlando, FL, USA, 04-07 Dec 2021, ISBN 9781728190488 (doi: 10.1109/SSCI50451.2021.9660193)

Abstract

Gait adaptation is an important part of gait analysis and its neuronal origin and dynamics has been studied extensively. In neurorehabilitation, it is important because it enables neuroplasticity mechanisms and facilitates the restoration of motor function. For this reason, brain-computer interfaces (BCI) have been build to facilitate neurorehabilitation. This paper presents a gait adaptation scheme in natural settings. It allows monitoring of subjects in more realistic environment without the requirement of specialized equipment such as treadmill and foot pressure sensors. We extract gait characteristics based on a single RGB camera whereas wireless EEG signals are monitored simultaneously. Based on Regularised Common Spatial Patterns (RCSP) that take into consideration both amplitude and frequency EEG features, we demonstrate that the method can not only successfully detect adaptation steps but it also detect efficiently whether the subject adjust their pace to higher or lower speed.

• Item Type: Conference Proceedings
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Deligianni, Dr Fani
• Authors: Domingos, I., Yang, G.-Z., and Deligianni, F.
• College/School: College of Science and Engineering > School of Computing Science
• ISBN: 9781728190488
• Copyright Holders: Copyright © 2021 The Authors
• First Published: First published in 2021 IEEE Symposium Series on Computational Intelligence (SSCI)
• Publisher Policy: Reproduced in accordance with the publisher copyright policy

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Project Code	Award No	Project Name	Principal Investigator	Funder's Name	Funder Ref	Lead Dept
301671		Developing the Human Data Interaction Framework	Matthew Chalmers	Engineering and Physical Sciences Research Council (EPSRC)	EP/R045178/1	Computing Science