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Calibrating Recurrent Neural Networks on Smartphone Inertial Sensors for Location Tracking

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Original languageEnglish
Title of host publication2019 International Conference on Indoor Positioning and Indoor Navigation (IPIN)
Number of pages8
Publication statusAccepted/In press - 30 Jun 2019
Event2019 International Conference on Indoor Positioning and Indoor Navigation (IPIN) - Pisa, Italy
Duration: 30 Sep 20193 Oct 2019


Conference2019 International Conference on Indoor Positioning and Indoor Navigation (IPIN)
Abbreviated titleIPIN 2019
Internet address


The need for location tracking in many mobile services has given rise to the broad research topic of indoor positioning we see today. However, the majority of proposed systems in this space is based on traditional approaches of signal processing and simple machine learning solutions. In the age of big data, it is imperative to evolve our techniques to learn the complexity of indoor environments directly from data with modern machine learning approaches inspired from deep learning. We model location tracking from smartphone inertial sensor data with recurrent neural networks. Through our broad experimentation we provide an empirical study of the best model configuration, data preprocessing and training process to achieve improved inference accuracy. Our explored solutions are lightweight to run efficiently under limited computing resources available on mobile devices, while also achieving accurate estimations, within 5 meters median error from inertial sensors alone.

ID: 112864049