Semi-supervised learning: Predicting activities in Android environment

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2012

Autores

Alexandre Lopes (Autor) Outra; João Mendes-Moreira (Autor) FEUP; João Gama (Autor) FEP

Ata de Conferência Internacional

Título: CEUR Workshop Proceedings

Páginas: 38-42

Workshop on Ubiquitous Data Mining, UDM 2012 - In Conjunction with the 20th European Conference on Artificial Intelligence, ECAI 2012

27 August 2012 through 31 August 2012

Indexação

Scopus - 2 Citações

Classificação Científica

FOS: Ciências da engenharia e tecnologias > Engenharia electrotécnica, electrónica e informática

CORDIS: Ciências Tecnológicas > Engenharia > Engenharia de computadores

Outras Informações

ID Authenticus: P-00A-8QC

Abstract (EN): Predicting activities from data gathered with sensors gained importance over the years with the objective of getting a better understanding of the human body. The purpose of this paper is to show that predicting activities on an Android phone is possible. We take into consideration different classifiers, their accuracy using different approaches (hierarchical and one step classification) and limitations of the mobile itself like battery and memory usage. A semi-supervised learning approach is taken in order to compare its results against supervised learning. The objective is to discover if the application can be adapted to the user providing a better solution for this problem. The activities predicted are the most usual in everyday life: walking, running, standing idle and sitting. An android prototype, embedding the software MOA, was developed to experimentally evaluate the ideas proposed here.

Idioma: Inglês

Tipo (Avaliação Docente): Científica