Resumen de Machine learning-based techniques for indoor localization and human activity recognition through wearable devices
This thesis approaches the study of several machine learning techniques to improve the performance of indoor positioning systems, with a special focus on wearable and low-cost devices. It also presents some tools designed to facilitate the research in this field through the development of a software framework for indoor positioning-related research, and the creation of a web platform committed to becoming a collaborative repository of data. The framework has been developed as an open-source package for the R language platform. This allows other users to collaborate in the development of future functionality.
