A 3D mobile positioning method based on deep learning for hospital applications
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RESEARCH
Open Access
A 3D mobile positioning method based on deep learning for hospital applications Qingqing Zhang*
and Yuan Wang
* Correspondence: suiyue2959@163. com School of Management, Xi’an Polytechnic University, Xi’an 710048, People’s Republic of China
Abstract In this study, a 3D positioning method is proposed for hospital applications, such as navigation within a hospital building. It employs deep learning algorithms to analyze the received signal strength from cellular networks and Wi-Fi access points in order to estimate the positions of mobile stations. A two-stage deep learning procedure (level classification and location determination) is constructed to obtain the exact position information (building level, longitude, and latitude) in multiple-level buildings. To evaluate the performance of the proposed method, an experiment was conducted in the hospital of Xi’an Polytechnic University. In total, 36,985 records, 42 sampling location points, 28 different cellular networks, and 289 different Wi-Fi access points were considered. A deep learning neural network was trained for the first stage of level classification. Three deep learning neural networks were trained to obtain the distinct location coordinates (longitude and latitude) for three different building levels. To compare the efficacy of heterogeneous networks, three kinds of neural networks with different inputs (only cellular, only Wi-Fi APs, and a conjunction of cellular and Wi-Fi APs) were implemented. The accuracy of level classification was shown to be 100% for only Wi-Fi APs as an input. The average distance error of the location determination for different floors was 0.28 m for only Wi-Fi APs and for the conjunction of Wi-Fi APs and cellular networks in the second stage. Keywords: Indoor positioning, Deep leaning, Mobile positioning method, Received signal strength
1 Introduction Global Positioning Systems (GPS) are the most well-known tool in navigation and positioning frameworks. However, they do not usually work in the interior of buildings. In the urban environment, the propagation of GPS satellite signals is hindered by buildings. The “Urban Canyon” effect prevents GPS from accurately predicting indoor positioning. Due to the complex indoor environment, indoor propagation of signals is more complicated than outdoor propagation. The positioning accuracy is required to be controllable within a few meters to provide users with the maximum utility. In view of the difficulties involved in indoor positioning and the excessive requirements for positioning accuracy, researchers have done a lot of work. These studies involve many © The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party
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