Calibrating Recurrent Neural Networks on Smartphone Inertial Sensors for Location Tracking

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.