9,265 research outputs found
Low-effort place recognition with WiFi fingerprints using deep learning
Using WiFi signals for indoor localization is the main localization modality
of the existing personal indoor localization systems operating on mobile
devices. WiFi fingerprinting is also used for mobile robots, as WiFi signals
are usually available indoors and can provide rough initial position estimate
or can be used together with other positioning systems. Currently, the best
solutions rely on filtering, manual data analysis, and time-consuming parameter
tuning to achieve reliable and accurate localization. In this work, we propose
to use deep neural networks to significantly lower the work-force burden of the
localization system design, while still achieving satisfactory results.
Assuming the state-of-the-art hierarchical approach, we employ the DNN system
for building/floor classification. We show that stacked autoencoders allow to
efficiently reduce the feature space in order to achieve robust and precise
classification. The proposed architecture is verified on the publicly available
UJIIndoorLoc dataset and the results are compared with other solutions
Hybrid Building/Floor Classification and Location Coordinates Regression Using A Single-Input and Multi-Output Deep Neural Network for Large-Scale Indoor Localization Based on Wi-Fi Fingerprinting
In this paper, we propose hybrid building/floor classification and
floor-level two-dimensional location coordinates regression using a
single-input and multi-output (SIMO) deep neural network (DNN) for large-scale
indoor localization based on Wi-Fi fingerprinting. The proposed scheme exploits
the different nature of the estimation of building/floor and floor-level
location coordinates and uses a different estimation framework for each task
with a dedicated output and hidden layers enabled by SIMO DNN architecture. We
carry out preliminary evaluation of the performance of the hybrid floor
classification and floor-level two-dimensional location coordinates regression
using new Wi-Fi crowdsourced fingerprinting datasets provided by Tampere
University of Technology (TUT), Finland, covering a single building with five
floors. Experimental results demonstrate that the proposed SIMO-DNN-based
hybrid classification/regression scheme outperforms existing schemes in terms
of both floor detection rate and mean positioning errors.Comment: 6 pages, 4 figures, 3rd International Workshop on GPU Computing and
AI (GCA'18
Accurate Real Time Localization Tracking in A Clinical Environment using Bluetooth Low Energy and Deep Learning
Deep learning has started to revolutionize several different industries, and
the applications of these methods in medicine are now becoming more
commonplace. 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). The performance of these networks was
compared to relative received signal strength indicator (RSSI) thresholding and
triangulation. 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%. It outperformed a CNN model (accuracy = 94%), a thresholding
model employing majority voting (accuracy = 95%), and a triangulation
classifier utilizing majority voting (accuracy = 95%). Future studies will seek
to deploy this affordable real time location system in hospitals to improve
clinical workflow, efficiency, and patient safety
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