UT Institute of Computer Science Graduation Theses Topics Registry

Organization

Machine learning and data mining

Abstract

Using data to understand where the people are is one of the fundamental tasks in a smart home. The standard motion sensors that are used in security systems do not provide good precision and localisation can be improved adding other sources of data. For this project we consider the setting where the people are wearing an acceleration sensor on the wrist and there are multiple access points receiving signals from it. The received signal strength allows to localise the person. Instead of modelling the complicated physics of how signal travels we take a much simpler approach - use machine learning. For this the person first goes to all corners of all rooms and carefully annotates the location. This can then be used as a training set to learn a localisation model using standard machine learning methods.

For this project we have access to several datasets, one of them is 1 month long, with the potential to localise the resident whenever he/she is at home. The goal is to learn a predictive model which is reasonably accurate in telling which room the person is in. Optionally, this could be followed by an attempt to improve precision further and predict where in the room the person is. The project has a good potential to be extended into a bachelor or master thesis.

Graduation Theses defence year

2017-2018

Supervisor

Meelis Kull

Spoken language (s)

Estonian, English

Requirements for candidates

Level

Bachelor, Masters

Keywords

 

Name

Meelis Kull

Phone

E-mail