Balancing Robustness and Responsiveness in a Real-time Indoor Location System using Bluetooth Low Energy Technology and Deep Learning to Facilitate Clinical Applications
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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 our testing data, significantly improved from LSTM without constraint which is 96.2 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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