【摘 要】
：
Wandering is a significant indicator in the clinical als\'indoor motion and reliably identify wandering movement in a non-invasive manner,in this work,we develop a LSTM-based deep classification method that is able to differentiate the wandering-caused
【机 构】
：
School of mathematics and computer science,Northwest Minzu University,Lanzhou 730030,China;Key Labor
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Wandering is a significant indicator in the clinical als\'indoor motion and reliably identify wandering movement in a non-invasive manner,in this work,we develop a LSTM-based deep classification method that is able to differentiate the wandering-caused Wi-Fi signal change from the others.Specif-ically,we first use the off-the-shelf Wi-Fi devices to capture a resident\'s indoor motion information,enabling to collect a group of Wi-Fi signal streams,which will be split into variable-size segments.Second,the deep network LSTM is adopted to develop wandering detection method that is able to classify ev-ery variable-size segment of Wi-Fi signals into categories ac-cording to the well-known wandering spatiotemporal patterns.Last,experimental evaluation conducted on a group of real-world Wi-Fi signal streams shows that our proposed LSTM-based detection method is workable and effective to identify in-door wandering behavior,obtaining an average value of 0.9286,0.9618,0.9634 and 0.9619 for accuracy,precision,recall and F-1 score,respectively.
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