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Sensor Fusion for Identification of Freezing of Gait Episodes Using Wi-Fi and Radar Imaging

Shah, Syed Aziz; Tahir, Ahsen; Ahmad, Jawad; Zahid, Adnan; Pervaiz, Haris; Shah, Syed Yaseen; Abdulhadi Ashleibta, Aboajeila Milad; Hasanali, Aamir; Khattak, Shadan; Abbasi, Qammer H.


Syed Aziz Shah

Ahsen Tahir

Adnan Zahid

Haris Pervaiz

Syed Yaseen Shah

Aboajeila Milad Abdulhadi Ashleibta

Aamir Hasanali

Shadan Khattak

Qammer H. Abbasi


Parkinson’s disease (PD) is a progressive 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.

Journal Article Type Article
Acceptance Date Jun 22, 2020
Online Publication Date Jun 24, 2020
Publication Date Dec 1, 2020
Deposit Date Nov 9, 2020
Publicly Available Date Nov 9, 2020
Journal IEEE Sensors Journal
Print ISSN 1530-437X
Electronic ISSN 1558-1748
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 20
Issue 23
Pages 14410-14422
Keywords Instrumentation; Electrical and Electronic Engineering
Public URL


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