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

Shah, Syed Aziz and Tahir, Ahsen and Ahmad, Jawad and Zahid, Adnan and Pervaiz, Haris and Shah, Syed Yaseen and Ashleibta, Aboajeila Milad Abdulhadi and Hasanali, Aamir and Khattak, Shadan and Abbasi, Qammer 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. ISSN 1530-437X

Abstract

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.