Machine learning-based techniques for indoor localization and human activity recognition through wearable devices

Autores: Emilio Sansano Sansano
Directores de la Tesis: Raúl Montoliu Colás (dir. tes.) , Óscar Belmonte Fernández (dir. tes.)
Lectura: En la Universitat Jaume I ( España ) en 2020
Idioma: español
Tribunal Calificador de la Tesis: Jesús Ureña Ureña (presid.) , J. S. Sanchez (secret.) , Estefanía Muñoz Díaz Ropero (voc.)
Enlaces
- Tesis en acceso abierto en: TDX
Resumen
- 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.