University of Oulu

Indoor outdoor detection

Saved in:
Author: Garcia Hurtado, Juan1
Organizations: 1University of Oulu, Faculty of Information Technology and Electrical Engineering, Department of Computer Science and Engineering, Computer Science and Engineering
Format: ebook
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 7 MB)
Pages: 46
Persistent link: http://urn.fi/URN:NBN:fi:oulu-201906062479
Language: English
Published: Oulu : J. Garcia Hurtado, 2019
Publish Date: 2019-06-11
Thesis type: Master's thesis
Tutor: Teixeira Ferreira, Denzil
Reviewer: Teixeira Ferreira, Denzil
Hosio, Simo
Description:

Abstract

This thesis shows a viable machine learning model that detects Indoor or Outdoor on smartphones. The model was designed as a classification problem and it was trained with data collected from several smartphone sensors by participants of a field trial conducted. The data collected was labeled manually either indoor or outdoor by the participants themselves. The model was then iterated over to lower the energy consumption by utilizing feature selection techniques and subsampling techniques. The model which uses all of the data achieved a 99 % prediction accuracy, while the energy efficient model achieved 92.91 %. This work provides the tools for researchers to quantify environmental exposure using smartphones.

see all

Subjects:
Copyright information: © Juan Garcia Hurtado, 2019. This publication is copyrighted. You may download, display and print it for your own personal use. Commercial use is prohibited.