WiFreeze: multiresolution scalograms for freezing of gait detection in Parkinson’s leveraging 5G spectrum with deep learning

Tahir, A., Ahmad, J., Shah, S. A. , Morison, G., Skelton, D. A., Larigani, H., Abbasi, Q. H. , Imran, M. A. and Gibson, R. M. (2019) WiFreeze: multiresolution scalograms for freezing of gait detection in Parkinson’s leveraging 5G spectrum with deep learning. Electronics, 8(12), 1433. (doi: 10.3390/electronics8121433)

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Abstract

Freezing of Gait (FOG) is an episodic absence of forward movement in Parkinson’s Disease (PD) patients and represents an onset of disabilities. FOG hinders daily activities and increases fall risk. There is high demand for automating the process of FOG detection due to its impact on health and well being of individuals. This work presents WiFreeze, a noninvasive, line of sight, and lighting agnostic WiFi-based sensing system, which exploits ambient 5G spectrum for detection and classification of FOG. The core idea is to utilize the amplitude variations of wireless Channel State Information (CSI) to differentiate between FOG and activities of daily life. A total of 225 events with 45 FOG cases are captured from 15 patients with the help of 30 subcarriers and classification is performed with a deep neural network. Multiresolution scalograms are proposed for time–frequency signatures of human activities, due to their ability to capture and detect transients in CSI signals caused by transitions in human movements. A very deep Convolutional Neural Network (CNN), VGG-8K, with 8K neurons each, in fully connected layers is engineered and proposed for transfer learning with multiresolution scalogram features for detection of FOG. The proposed WiFreeze system outperforms all existing wearable and vision-based systems as well as deep CNN architectures with the highest accuracy of 99.7% for FOG detection. Furthermore, the proposed system provides the highest classification accuracies of 94.3% for voluntary stop and 97.6% for walking slow activities, with improvements of 9% and 23%, respectively, over the best performing state-of-the-art deep CNN architecture.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Abbasi, Dr Qammer and Imran, Professor Muhammad and Shah, Mr Syed
Creator Roles:
Shah, S. A.Methodology, Resources
Abbasi, Q. H.Project administration
Imran, M. A.Project administration
Authors: Tahir, A., Ahmad, J., Shah, S. A., Morison, G., Skelton, D. A., Larigani, H., Abbasi, Q. H., Imran, M. A., and Gibson, R. M.
College/School:College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
College of Science and Engineering > School of Engineering > Systems Power and Energy
Journal Name:Electronics
Publisher:MDPI
ISSN:2079-9292
ISSN (Online):2079-9292
Copyright Holders:Copyright © 2019 The Authors
First Published:First published in Electronics 8(12):1433
Publisher Policy:Reproduced under a Creative Commons licence

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