Human Activity Recognition via Score Level Fusion of Wi-Fi CSI Signals

Abstract

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.

Ambient intelligent framework for modelling critical medical events based on context awareness

“…From the viewpoint of contextual awareness towards activity detection, there is a higher degree of variations associated with user activity with a lack of personalization and adaptation, thereby making it quite challenging to construct a universal model [11]- [15]. Another significant challenge is constructing a comprehensive contextual model towards an activity determination system by integrating information from multiple sources [16]- [20]. Further, a dynamic environment always acts as an impediment towards effective implications of the model functioning.…”