  Spatial navigation of indoor space usage patterns reveals important cues
about the cognitive health of individuals. In this work, we present a low-cost,
scalable, open-source edge computing system using Bluetooth Low Energy (BLE)
and Inertial Measurement Unit sensors (IMU) for tracking indoor movements for a
large indoor facility (over 1600 m^2) that was designed to facilitate
therapeutic activities for individuals with Mild Cognitive Impairment. The
facility is instrumented with 39 edge computing systems with an on-premise fog
server, and subjects carry BLE beacon and IMU sensors on-body. We proposed an
adaptive trilateration approach that considers the temporal density of hits
from the BLE beacon to surrounding edge devices to handle inconsistent coverage
of edge devices in large spaces with varying signal strength that leads to
intermittent detection of beacons. The proposed BLE-based localization is
further enhanced by fusing with an IMU-based tracking method using a
dead-reckoning technique. Our experiment results, achieved in a real clinical
environment, suggest that an ordinary medical facility can be transformed into
a smart space that enables automatic assessment of the individual patients'
movements.
