Indoor Localization using Bluetooth and Inertial Motion Sensors in Distributed Edge and Cloud Computing Environment

TL;DR

This paper presents a scalable, low-cost indoor localization system using Bluetooth and inertial sensors in a distributed edge and cloud environment, enabling automatic movement assessment in large clinical spaces.

Contribution

It introduces an adaptive trilateration method combined with IMU fusion for improved BLE-based indoor localization in large, complex environments.

Findings

Effective localization in large indoor spaces with intermittent BLE signals

Fusion of BLE and IMU improves tracking accuracy

System enables automatic movement assessment in clinical settings

Abstract

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.