Wireless Sensing with Machine Learning: Through-Wall Vision & Contactless Health Monitoring

There is significant interest in technologies that can sense people and monitor their health with minimal overhead. Existing solutions typically require people to wear different sensors and devices on their bodies. This thesis demonstrates how we can use wireless signals and machine learning to sense people without any physical contact with their bodies. We develop novel radio sensors that sit in the background like a Wi-Fi router. Our sensors however analyze the surrounding radio signals using novel machine learning algorithms to monitor people’s movements and activities, assess their vital signs, learn their sleep and sleep stages, and recognize their emotions. Since wireless sensors traverse walls, our sensors can deliver all of these functions through walls and occlusions. The key challenge in delivering the above contributions is that radio signals interact with people and the environment in complex ways, resulting in an underdetermined mapping that varies across time and space. To address this problem, this dissertation adopts a data-driven approach and develops custom machine learning models that operate on radio signals. Developing such models requires technical innovations to address unique challenges due to the specularity of radio signals in the frequencies of interest, multipath reflections in indoor environments, high data rates and computation complexity, and the lack of training data and the difficulty in annotating radio signals. Our work addresses these challenges and enables two new capabilities: through-wall tracking of the human pose and contactless health monitoring.

Name thesis:degree_name

Doctoral

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

2022

Author dc:creator

• Zhao, Mingmin

Advisor dc:contributor.advisor

• Katabi, Dina

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright MIT

Licence dc:rights.uri

• http://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/143370

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/143370