Journal
SENSORS
Volume 23, Issue 16, Pages -Publisher
MDPI
DOI: 10.3390/s23167292
Keywords
human activity recognition; Wi-Fi CSI signals; score-level fusion
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In this study, a score-level fusion structure using Wi-Fi channel state information (CSI) signals for human activity recognition is investigated. The fusion provides a convenient, covert, and non-invasive means of recognizing human activity, particularly useful for healthcare monitoring. Experimental results show that the fusion provides good generalization and a shorter learning processing time compared with state-of-the-art networks.
Wi-Fi signals are ubiquitous and provide a convenient, covert, and non-invasive means of recognizing human activity, which is particularly useful for healthcare monitoring. In this study, we investigate a score-level fusion structure for human activity recognition using the Wi-Fi channel state information (CSI) signals. The raw CSI signals undergo an important preprocessing stage before being classified using conventional classifiers at the first level. The output scores of two conventional classifiers are then fused via an analytic network that does not require iterative search for learning. Our experimental results show that the fusion provides good generalization and a shorter learning processing time compared with state-of-the-art networks.
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