Human activity analysis using radio signals

Understanding people's actions and interactions typically depends on seeing them. Automating the process of human action recognition and event captioning from visual data has been the topic of much research in the computer vision community. But what if it is too dark, or if the person is occluded or behind a wall? This thesis develops a model that can detect human actions through walls and occlusions, and in poor lighting conditions. The model takes radio frequency (RF) signals as input, generates 3D human skeletons as an intermediate representation, and recognizes actions and interactions of multiple people over time, or even generate language descriptions for the event. By translating the input to an intermediate skeleton-based representation, our model can learn from both vision-based and RF-based datasets. This thesis also introduces a new model for captioning daily life by analyzing RF signal in the home with the home's floormap. It can further observe and caption people's life through walls and occlusions and in dark settings. We show that our model achieves comparable accuracy to vision-based action recognition systems in visible scenarios, yet continues to work accurately when people are not visible, hence addressing scenarios that are beyond the limit of today's vision-based action recognition.

Name thesis:degree_name

Master

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

2020

Author dc:creator

• Fan, Lijie(Biologist)Massachusetts Institute of Technology.

Advisor dc:contributor.advisor

• Dina Katabi.

• Electrical Engineering and Computer Science.

Statement dc:rights

• MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.

Licence dc:rights.uri

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

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/127341

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/127341