Sensor fusion for identification of freezing of gait episodes using Wi-Fi and radar imaging

Shah, S. A. , Tahir, A., Ahmad, J., Zahid, A., Parvez, H., Shah, S. Y., Ashleibta, A. M. A., Hasanali, A., Khattak, S. and Abbasi, Q. H. (2020) Sensor fusion for identification of freezing of gait episodes using Wi-Fi and radar imaging. IEEE Sensors Journal, 20(23), pp. 14410-14422. (doi: 10.1109/JSEN.2020.3004767)

Abstract

Parkinson’s disease (PD) is a progress and neurodegenerative condition causing motor impairments. One of the major motor related impairments that present biggest challenge is freezing of gait (FOG) in Parkinson’s patients. In FOG episode, the patient is unable to initiate, control or sustain a gait that consequently affects the Activities of Daily Livings (ADLs) and increases the occurrence of critical events such as falls. This paper presents continuous monitoring ADLs and classification freezing of gait episodes using Wi-Fi and radar imaging. The idea is to exploit the multi-resolution scalograms generated by channel state information (CSI) imprint and micro-Doppler signatures produced by reflected radar signal. A total of 120 volunteers took part in experimental campaign and were asked to perform different activities including walking fast, walking slow, voluntary stop, sitting down & stand up and freezing of gait. Two neural networks namely Autoencoder and a proposed enhanced Autoencoder were used classify ADLs and FOG episodes using data fusion process by combining the images acquired from both sensing techniques. The Autoencoder provided overall classification accuracy of ~87% for combined datasets. The proposed algorithm provided significantly better results by presenting an overall accuracy of ~98% using data fusion.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Abbasi, Professor Qammer and Zahid, Mr Adnan and Shah, Mr Syed and Ashleibta, Aboajeila Milad Abdulhadi
• Authors: Shah, S. A., Tahir, A., Ahmad, J., Zahid, A., Parvez, H., Shah, S. Y., Ashleibta, A. M. A., Hasanali, A., Khattak, S., and Abbasi, Q. H.
• 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: IEEE Sensors Journal
• Publisher: IEEE
• ISSN: 1530-437X
• ISSN (Online): 1558-1748
• Published Online: 24 June 2020
• Copyright Holders: Copyright © 2020 IEEE
• First Published: First published in IEEE Sensors Journal 20(23):14410-14422
• Publisher Policy: Reproduced under a Creative Commons License

Project Code	Award No	Project Name	Principal Investigator	Funder's Name	Funder Ref	Lead Dept
172865		EPSRC DTP 16/17 and 17/18	Tania Galabova	Engineering and Physical Sciences Research Council (EPSRC)	EP/N509668/1	Research and Innovation Services