'Institute of Electrical and Electronics Engineers (IEEE)'
Abstract
The pervasive deployment of connected devices in modern society has significantly changed the nature of the wireless landscape, especially in the license free industrial, scientific and medical (ISM) bands. This paper introduces a deep learning enabled passive radio sensing method that can monitor human respiration and daily activities through leveraging unplanned and ever-present wireless bursts in the ISM frequency band, and can be employed as an additional data input within healthcare informatics. Wireless connected biomedical sensors (Medical Things) rely on coding and modulating of the sensor data onto wireless (radio) bursts which comply with specific physical layer standards like 802.11, 802.15.1 or 802.15.4. The increasing use of these unplanned connected sensors has led to a pell-mell of radio bursts which limit the capacity and robustness of communication channels to deliver data, whilst also increasing inter-system interference. This paper presents a novel methodology to disentangle the chaotic bursts in congested radio environments in order to provide healthcare informatics. The radio bursts are treated as pseudo noise waveforms which eliminate the requirement to extract embedded information through signal demodulation or decoding. Instead, we leverage the phase and frequency components of these radio bursts in conjunction with cross ambiguity function (CAF) processing and a Deep Transfer Network (DTN). We use 2.4GHz 802.11 (WiFi) signals to demonstrate experimentally the capability of this technique for human respiration detection (including through-the-wall), and classifying everyday but complex human motions such as standing, sitting and falling
