Final published version
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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Sensor Fusion for Identification of Freezing of Gait Episodes Using Wi-Fi and Radar Imaging
AU - Shah, Syed Aziz
AU - Tahir, Ahsen
AU - Ahmad, Jawad
AU - Zahid, Adnan
AU - Pervaiz, Haris
AU - Shah, Syed Yaseen
AU - Ashleibta, Aboajeila Milad Abdulhadi
AU - Hasanali, Aamir
AU - Khattak, Shadan
AU - Abbasi, Qammer H.
PY - 2020/12/1
Y1 - 2020/12/1
N2 - 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.
AB - 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.
U2 - 10.1109/JSEN.2020.3004767
DO - 10.1109/JSEN.2020.3004767
M3 - Journal article
VL - 20
SP - 14410
EP - 14422
JO - IEEE Sensors Journal
JF - IEEE Sensors Journal
SN - 1530-437X
IS - 23
ER -