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Sensor Fusion for I...
Sensor Fusion for Identification of Freezing of Gait Episodes Using Wi-Fi and Radar Imaging
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- Shah, Syed Aziz (författare)
- Coventry Univ, England
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- Tahir, Ahsen (författare)
- Edinburgh Napier Univ, Scotland
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- Ahmad, Jawad (författare)
- Edinburgh Napier Univ, Scotland
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- Zahid, Adnan (författare)
- Univ Glasgow, Scotland
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- Pervaiz, Haris (författare)
- Univ Lancaster, England
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- Shah, Syed Yaseen (författare)
- Glasgow Caledonian Univ, Scotland
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- Abdulhadi Ashleibta, Aboajeila Milad (författare)
- Univ Glasgow, Scotland
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- Hasanali, Aamir (författare)
- Linköpings universitet
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- Khattak, Shadan (författare)
- King Faisal Univ, Saudi Arabia
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- Abbasi, Qammer H. (författare)
- Univ Glasgow, Scotland
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(creator_code:org_t)
- Institute of Electrical and Electronics Engineers (IEEE), 2020
- 2020
- Engelska.
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Ingår i: IEEE Sensors Journal. - : Institute of Electrical and Electronics Engineers (IEEE). - 1530-437X .- 1558-1748. ; 20:23, s. 14410-14422
- Relaterad länk:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- Parkinsons 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 Parkinsons 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 similar to 87% for combined datasets. The proposed algorithm provided significantly better results by presenting an overall accuracy of similar to 98% using data fusion.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Signalbehandling (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Signal Processing (hsv//eng)
Nyckelord
- Sensors; Radar; OFDM; Wireless fidelity; Diseases; Frequency modulation; Radar sensing; Wi-Fi sensing; deep learning; FOG detection
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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