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.

利用低成本的 Ultra-Wide Band（UWB）传感器和深度学习网络，我们提出了一个人类状态估计框架，能够在室内环境中无需特定设备的情况下估计人的位置和活动，实现精确的定位和活动识别。