A Learning-Based Approach Towards Localization of Crowdsourced Motion-Data for Indoor Localization Applications

Many popular fingerprinting-based indoor localization methods, such as WiFi-based localization systems, rely on a dataset of fingerprints labelled by known locations (fingerprint-location dataset) to be able to localize a user by comparing the queried fingerprint with the fingerprints in the dataset. Generating and updating such a dataset is a burden and requires significant amount of time, human effort and expertise. In this work, we propose a system to build such a dataset from scratch using crowdsourced data. Consequently we reduce the required time and effort, by leveraging the information shared by the crowd. The proposed system is based on localization of user motion, hence called LocaMotion. LocaMotion takes a supervised learning perspective towards localization of user-sent motion data. In other words, LocaMotion is formalised as a classifier that extracts and assigns features from a user motion data to a sequence of points in the building. Training and testing procedures for LocaMotion will be discussed in details followed by extensive experiments to demonstrate its validity and success to localize motion patterns and generate useful fingerprint-location datasets.