Adding identity to device-free localization systems

Recent advances in wireless localization systems show that by transmitting a wireless signal and analyzing its reflections, one can localize a person and track her vital signs without any wearables. These systems can localize with high accuracy even when multiple people are present in the environment. However, a primary limitation is that they cannot identify people and know who is the monitored person. In this thesis, we present a system for identifying people based only on their wireless reflection with high accuracy. We use a semi-supervised learning classifier to assign labels to each person tracked by the device-free localization system. We use recent advances in machine learning to leverage the big amount of unsupervised data that we have. A key challenge that we solve is obtaining labels that are used for guiding the classifier. To get labeled data, we devised a novel scheme to combine data from a sensor that people are carrying with data from a wireless localization system. We deployed and evaluated our system in people's homes. We present a case study of how it can be helpful to monitor people's health more effectively.

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2017

Author dc:creator

• Hristov, Rumen (Rumen H.)

Advisor dc:contributor.advisor

• Dina Katabi.

• Electrical Engineering and Computer Science.

Statement dc:rights

• MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/119523

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/119523