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

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

    Research output: Contribution to journalArticlepeer-review

    33 Citations (Scopus)
    45 Downloads (Pure)


    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.

    Original languageEnglish
    Article number9123933
    Pages (from-to)14410-14422
    Number of pages13
    JournalIEEE Sensors Journal
    Issue number23
    Early online date24 Jun 2020
    Publication statusPublished - 1 Dec 2020


    EPSRC DTG under Grant EP/N509668/1 Eng, Grant EP/T021020/1, and Grant EP/T021063/1.


    • Radar sensing
    • Wi-Fi sensing
    • deep learning
    • FOG detection

    ASJC Scopus subject areas

    • Instrumentation
    • Electrical and Electronic Engineering


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