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dc.contributor.advisorDina Katabi.en_US
dc.contributor.authorHsu, Chen-Yu,Ph. D.Massachusetts Institute of Technology.en_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.en_US
dc.date.accessioned2020-09-03T17:42:20Z
dc.date.available2020-09-03T17:42:20Z
dc.date.copyright2020en_US
dc.date.issued2020en_US
dc.identifier.urihttps://hdl.handle.net/1721.1/127020
dc.descriptionThesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, May, 2020en_US
dc.descriptionCataloged from the official PDF of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 151-168).en_US
dc.description.abstractLearning people's behavior in their homes is central to health sensing, behavioral research, and building smarter environments. In this thesis, we explore learning such information in a passive and contactless manner - without asking people to wear sensors on their bodies or change the way they normally live. We leverage that radio frequency (RF) signals bounce off people, and carry information about them. This thesis presents systems, algorithms, and machine learning models to analyze the signals in the environment and infer information about people's behavior and well-being. Specifically, we analyze the surrounding RF signals to infer people's movement patterns and enable continuous monitoring of gait velocity and stride length. We also sense people's sleep efficiency, sleep onset, and nocturnal awakenings using radio signals, without any wearable devices. Further, we demonstrate that radio signals carry information about people's identity and body shape. This thesis introduces the first system that reconstructs a person's silhouette using RF signals. We then develop this system further to identify users in their homes with no restrictions on their movement patterns. This thesis also shows that the combination of identity and movements allows us to analyze user behavior and interaction at home, without asking users to write diaries or deploy cameras in their living space. Finally, we introduce a new self-supervised learning method to infer appliance usage at home. Collectively, the models and systems in this thesis provide a toolkit for learning behavioral analytics at home from the surrounding radio signals, and addressing questions like who, what, and when, in a passive manner with minimal interference with users' lives.en_US
dc.description.statementofresponsibilityby Chen-Yu Hsu.en_US
dc.format.extent168 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsMIT 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.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectElectrical Engineering and Computer Science.en_US
dc.titlePassive sensing of user behavior and Well-being at homeen_US
dc.typeThesisen_US
dc.description.degreePh. D.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.identifier.oclc1191624908en_US
dc.description.collectionPh.D. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Scienceen_US
dspace.imported2020-09-03T17:42:20Zen_US
mit.thesis.degreeDoctoralen_US
mit.thesis.departmentEECSen_US


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