Development of a real‐time indoor location system using bluetooth low energy technology and deep learning to facilitate clinical applications. Issue 8 (11th May 2020) - Tang, Guanglin; Yan, Yulong; Shen, Chenyang; Jia, Xun; Zinn, Meyer; Trivedi, Zipalkumar; Yingling, Alicia; Westover, Kenneth; Jiang, Steve
Abstract : Purpose: 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 because Bluetooth signal is subject to significant fluctuation. We aim to improve the accuracy of RTLS using the deep learning technique. Methods: We installed a Bluetooth sensor network in a three‐floor clinic building to track patients, staff, and devices. The Bluetooth sensors measured the strength of the signal broadcasted from Bluetooth tags, which was fed into a deep neural network to calculate the location of the tags. The proposed deep neural network consists of a long short‐term memory (LSTM) network and a deep classifier for tracking moving objects. Additionally, a spatial‐temporal constraint algorithm was implemented to further increase the accuracy and stability of the results. To train the neural network, we divided the building into 115 zones and collected training data in each zone. We further augmented the training data to generate cross‐zone trajectories, mimicking the real‐world scenarios. We tuned the parameters for the proposed neural network to achieve relatively good accuracy. Results: The proposed deep neural network achieved an overall accuracy of about 97% for tracking objects in each individual zone in the whole three‐floor building, 1.5% higher than the baseline neural network that was proposed in an earlier paper, when using 10 s of signals. The accuracy increased with the density of Bluetooth sensors. For tracking moving objects, the proposed neural network achieved stable and accurate results. When latency is less of a concern, we eliminated the effect of latency from the accuracy and gained an accuracy of 100% for our testing trajectories, significantly improved from the baseline method. Conclusions: The proposed deep neural network composed of a LSTM, a deep classifier and a posterior constraint algorithm significantly improved the accuracy and stability of RTLS for tracking moving objects.