Motion discrimination by ambient cellular signals: machine learning and computing tools

Abstract

In this paper, we evaluate the capability of built-in cellular radio modems available in several IoT modules to track body motions in their close surroundings, by exploiting the real-time analysis of the omnipresent ambient (or stray) cellular signals. In fact, cellular-based IoT devices constantly monitor and report the received signal quality of the camped and neighbor cells for communication functionality imposed by the cellular standards. These quality signals are extracted and processed here to detect changes in the area nearby. A JSON-REST platform and computing infrastructure have been designed to efficiently store and manipulate in real-time these data samples. Experiments and system validation results are presented for a specific case study where two cellular-enabled devices are converted into sensors, while the cellular signal quality is tracked continuously for classifying body motions.