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Graph Trilateration for Indoor Localization in Sparsely Distributed Edge Computing Devices in Complex Environments
Yashar Kiarashi1, Soheil Saghafi1, Barun Das1
1Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, GA 30322, USA.
Sensors (Basel, Switzerland)
|December 9, 2023
Summary
This study introduces a low-cost edge computing system using Bluetooth low energy (BLE) beacons to track indoor movements for individuals with mild cognitive impairment (MCI). The system accurately assesses spatial navigation, aiding in monitoring cognitive health and treatment response.
Area of Science:
- Biomedical Engineering
- Computer Science
- Cognitive Science
Background:
- Spatial navigation patterns offer insights into cognitive health.
- Mild cognitive impairment (MCI) affects spatial abilities.
- Existing indoor tracking methods may lack scalability or accuracy in complex environments.
Purpose of the Study:
- To develop and evaluate a low-cost, scalable, open-source edge computing system for tracking indoor movements.
- To assess the cognitive health and treatment response of participants with MCI through spatial navigation analysis.
- To overcome challenges in indoor localization due to sparse sensor distribution.
Main Methods:
- Implementation of an edge computing system with 39 Bluetooth low energy (BLE) beacons and a fog server in a 1700 m2 facility.
- Development of a graph trilateration approach considering temporal beacon hit density to address sparse edge device coverage.
- Analysis of BLE signal reception by edge computing systems carried by participants.
Main Results:
- Achieved an average localization error of 4.4 meters for multiple participants.
- Reached over 85% accuracy in region-level localization across the study area.
- Demonstrated the system's effectiveness in a clinical environment with varying signal strengths and intermittent beacon detection.
Conclusions:
- An ordinary medical facility can be transformed into a smart space for automatic movement assessment.
- The developed system enables objective monitoring of spatial navigation, potentially reflecting health status or treatment efficacy in individuals with MCI.
- The graph trilateration method provides a robust solution for indoor localization in sparsely instrumented environments.
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