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Open AccessFeature PaperArticle

WiFreeze: Multiresolution Scalograms for Freezing of Gait Detection in Parkinson’s Leveraging 5G Spectrum with Deep Learning

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School of Computing, Engineering and Built Environment, Glasgow Caledonian University, Glasgow G4 0BA, UK
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Department of Electrical Engineering, University of Engineering and Technology, Lahore, Punjab 54890, Pakistan
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School of Computing, Edinburgh Napier University, Edinburgh EH10 5DT, UK
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School of Computing and Mathematics, Manchester Metropolitan University, Manchester M15 6BH, UK
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School of Health and Life Sciences, Glasgow Caledonian University, Glasgow G4 0BA, UK
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School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK
*
Author to whom correspondence should be addressed.
Electronics 2019, 8(12), 1433; https://doi.org/10.3390/electronics8121433
Received: 30 September 2019 / Revised: 8 November 2019 / Accepted: 11 November 2019 / Published: 1 December 2019
(This article belongs to the Section Microwave and Wireless Communications)
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. View Full-Text
Keywords: freezing of gate; deep learning; classification; WiFi sensing freezing of gate; deep learning; classification; WiFi sensing
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Tahir, A.; Ahmad, J.; Shah, S.A.; Morison, G.; Skelton, D.A.; Larijani, H.; Abbasi, Q.H.; Imran, M.A.; Gibson, R.M. WiFreeze: Multiresolution Scalograms for Freezing of Gait Detection in Parkinson’s Leveraging 5G Spectrum with Deep Learning. Electronics 2019, 8, 1433.

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