# Development of a Real-time Indoor Location System using Bluetooth Low Energy Technology and Deep Learning to Facilitate Clinical Applications

## TL;DR

This paper presents a real-time indoor location system using Bluetooth Low Energy and deep learning, enhancing accuracy and robustness for clinical applications through a novel machine learning approach and a posterior constraint algorithm.

## Contribution

The study introduces a machine learning-based RTLS with a history-based constraint algorithm, improving robustness and accuracy over traditional Bluetooth signal strength methods.

## Key findings

- LSTM outperforms other models in zone classification
- Posterior constraint reduces erratic zone switching
- Achieved 100% latency-corrected accuracy in testing

## Abstract

An indoor, real-time location system (RTLS) can benefit both hospitals and patients by improving clinical efficiency through data-driven optimization of procedures. Bluetooth-based RTLS systems are cost-effective but lack accuracy and robustness because Bluetooth signal strength is subject to fluctuation. We developed a machine learning-based solution using a Long Short-Term Memory (LSTM) network followed by a Multilayer Perceptron classifier and a posterior constraint algorithm to improve RTLS performance. Training and validation datasets showed that most machine learning models perform well in classifying individual location zones, although LSTM was most reliable. However, when faced with data indicating cross-zone trajectories, all models showed erratic zone switching. Thus, we implemented a history-based posterior constraint algorithm to reduce the variability in exchange for a slight decrease in responsiveness. This network increases robustness at the expense of latency. When latency is less of a concern, we computed the latency-corrected accuracy which is 100% for our testing data, significantly improved from LSTM without constraint which is 96.2%. The balance between robustness and responsiveness can be considered and adjusted on a case-by-case basis, according to the specific needs of downstream clinical applications. This system was deployed and validated in an academic medical center. Industry best practices enabled system scaling without substantial compromises to performance or cost.

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Source: https://tomesphere.com/paper/1907.10554