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Finally, the paper is concluded with some remarks as well as future research trends.Ĭhannel state information (CSI) provides a fine-grained description of the signal propagation process, which has attracted extensive attention in the field of indoor positioning. These challenges along with the common implementation pitfalls are discussed. While incorporating deep learning into fingerprinting has resulted in significant improvements, doing so, has also introduced new challenges. Since data is key in fingerprinting, a detailed review of publicly available indoor positioning datasets is presented. The solutions proposed in the literature are then analyzed, categorized, and compared against various performance evaluation metrics. First, the advantages and disadvantages of various fingerprint types for indoor positioning are discussed. This paper provides a comprehensive review of deep learning methods in indoor positioning. Recently, the research community started utilizing deep learning methods for fingerprinting after witnessing the great success and superiority these methods have over traditional/shallow machine learning algorithms. In the past, shallow learning algorithms were traditionally used in location fingerprinting. Location fingerprinting, which utilizes machine learning, has emerged as a viable method and solution for indoor positioning due to its simple concept and accurate performance. Outdoor positioning systems based on the Global Navigation Satellite System have several shortcomings that have deemed their use for indoor positioning impractical. Future studies will seek to deploy this affordable real time location system in hospitals to improve clinical workflow, efficiency, and patient safety. It outperformed a CNN model (accuracy = 94%), a thresholding model employing majority voting (accuracy = 95%), and a triangulation classifier utilizing majority voting (accuracy = 91%). By utilizing temporal information, a combined CNN+ANN network was capable of correctly identifying the location of the BLE tag with an accuracy of 99.9%.
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The performance of these networks was compared to relative received signal strength indicator (RSSI) thresholding and triangulation.
RADBEACON DOT HOLDER BLUETOOTH
This study focuses on investigating the feasibility of tracking patients and clinical staff wearing Bluetooth Low Energy (BLE) tags in a radiation oncology clinic using artificial neural networks (ANNs) and convolutional neural networks (CNNs). Deep learning has started to revolutionize several different industries, and the applications of these methods in medicine are now becoming more commonplace.