Vital Radio: RF-Based Through the Wall Motion Sensing and Scene Analysis

Abstract

In this project we presents a two-dimensional phase extraction system using passive WiFi sensing, coupled with end-to-end deep learning framework to moni- tor three basic elderly care activities namely: breathing rate, essential tremor and falls. Speci cally, a WiFi signal is acquired through two channels where the rst channel is the reference one, whereas the other signal is acquired by a passive re- ceiver after re ection from the human target. Adaptive lter is performed to make the surveillance signal source-data invariant by eliminating the echoes of the direct transmitted signal. We propose a novel convolutional neural network to classify the complex time series data and determine if it corresponds a to any of three mentioned activities, followed by a two dimensional phase extraction platform to determine and track the activity, lastly, benchmarking with random forest estimator is also per- formed. We collect an extensive dataset to train the learning models and develop reference benchmarks for the future studies in the eld. Using signal processing of cross-ambiguity function, various features in the signal are extracted. The en- tire implementations are performed using software de ned radios having directional antennas. We report the accuracy of our system in di erent conditions and envi- ronments and show that breathing rate can be measured with an accuracy of 87% when there are no obstacles. We also show a 98% accuracy in detecting falls and 93% accuracy in classifying tremor. The results indicate that passive WiFi systems coupled with deep learning show a great promise in replacing typical invasive health devices as standard tools for health care.