Detecting Freezing of Gait with earables trained from VR motion capture data

Oishi, Nobuyuki, Heimler, Benedetta, Pellatt, Lloyd, Plotnik, Meir and Roggen, Daniel (2021) Detecting Freezing of Gait with earables trained from VR motion capture data. 2021 International Symposium on Wearable Computers (ISWC '21), Virtual, 21 - 26 Sept 2021. Published in: ISWC '21: 2021 International Symposium on Wearable Computers. 33-37. Association for Computing Machinery, New York, NY, USA. ISBN 9781450384629

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Abstract

Freezing of Gait (FoG) is a common disabling motor symptom in Parkinson’s Disease (PD). Auditory cueing provided when FoG is detected can help mitigate the condition, for which earables are potentially well suited as they are capable of motion sensing and audio feedback. However, there are no studies so far on FoG detection at the ear. Immersive Virtual Reality (VR) combined with video-based full-body motion capture has been increasingly used to run FoG studies in the medical community. While there are motion capture datasets collected in such an environment, there are no datasets collected from IMU placed at the ear. In this paper, we show how to transfer such motion capture datasets to IMU domain and evaluate the capability of FoG detection from ear position in an immersive VR environment. Using a dataset of 6 PD patients, we compare machine learning-based FoG detection applied to the motion capture data and the virtual IMU. We have achieved an average sensitivity of 80.3% and an average specificity of 87.6% on FoG detection using the virtual earable IMU, which indicates the potential of FoG detection at the ear. This study is a step toward user-studies with earables in the VR setup, prior to conducting research in over-ground walking and everyday life.

Item Type: Conference Proceedings
Keywords: Freezing of Gait, Virtual IMU, Earables, Parkinson's Disease
Schools and Departments: School of Engineering and Informatics > Engineering and Design
Research Centres and Groups: Sensor Technology Research Centre
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SWORD Depositor: Mx Elements Account
Depositing User: Mx Elements Account
Date Deposited: 10 Aug 2021 09:14
Last Modified: 15 Oct 2021 11:14
URI: http://sro.sussex.ac.uk/id/eprint/101041

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