期刊
IEEE SENSORS JOURNAL
卷 20, 期 23, 页码 14410-14422出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSEN.2020.3004767
关键词
Sensors; Radar; OFDM; Wireless fidelity; Diseases; Frequency modulation; Radar sensing; Wi-Fi sensing; deep learning; FOG detection
资金
- EPSRC DTG [EP/N509668/1 Eng, EP/T021020/1, EP/T021063/1]
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 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.
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