Device-Free Human State Estimation using UWB Multi-Static Radios
Saria Al Laham Bobak H. Baghi Pierre-Yves Lajoie Amal Feriani Sachini Herath Steve Liu Gregory Dudek
We present a human state estimation framework that allows us to estimate the
location, and even the activities, of people in an indoor environment without
the requirement that they carry a specific devices with them. To achieve this
"device free" localization we use a small number of low-cost Ultra-Wide Band
(UWB) sensors distributed across the environment of interest. To achieve high
quality estimation from the UWB signals merely reflected of people in the
environment, we exploit a deep network that can learn to make inferences. The
hardware setup consists of commercial off-the-shelf (COTS) single antenna UWB
modules for sensing, paired with Raspberry PI units for computational
processing and data transfer. We make use of the channel impulse response (CIR)
measurements from the UWB sensors to estimate the human state - comprised of
location and activity - in a given area. Additionally, we can also estimate the
number of humans that occupy this region of interest. In our approach, first,
we pre-process the CIR data which involves meticulous aggregation of
measurements and extraction of key statistics. Afterwards, we leverage a
convolutional deep neural network to map the CIRs into precise location
estimates with sub-30 cm accuracy. Similarly, we achieve accurate human
activity recognition and occupancy counting results. We show that we can
quickly fine-tune our model for new out-of-distribution users, a process that
requires only a few minutes of data and a few epochs of training. Our results
show that UWB is a promising solution for adaptable smart-home localization and
activity recognition problems.