Graduation Semester and Year




Document Type


Degree Name

Doctor of Philosophy in Computer Science


Computer Science and Engineering

First Advisor

Fillia Makedon


Tracking the location of a user is considered to be the most fundamental step for creating a context aware application, such as activity monitoring in an assistive environment. This problem becomes very challenging if there are multiple people involved in this scenario. The reason is that any multi-person environment, such as a hospital, demands simultaneous identification and localization mechanisms, thus making the system very complex. In this dissertation, we present a novel, less-intrusive system that uses RFIDs and sensors deployed at various locations of an assistive apartment to continuously track and identify every person in a multi-person assistive environment. Our experimental result proves the prospect of using RFID and sensors jointly to solve the simultaneous tracking and identification problem. In addition, this system stores the large scale spatio-temporal sensor data into a common repository and provides a flexible query interface to track the history of the patient. The visualization tool embedded in the system helps therapists to remotely monitor a person present in a scene in near real time. Such a visualization gives a very good indication about a person/patient's activity and behavior in the assistive environment. Our system also incorporates the metadata mapping of the large amount of stored data so that a doctor/therapist can query about a patient's records without having a complete knowledge about the schemas stored in the repository.


Computer Sciences | Physical Sciences and Mathematics


Degree granted by The University of Texas at Arlington