Faculty Advisor or Committee Member

Kaveh Pahlavan, Advisor

Faculty Advisor or Committee Member

Allen H. Levesque, Committee Member

Faculty Advisor or Committee Member

Kamran Sayrafian, Committee Member

Faculty Advisor or Committee Member

Yehia Massoud, Department Head

Faculty Advisor or Committee Member

Lifeng Lai, Committee Member

Faculty Advisor or Committee Member

Emmanuel O. Agu, Committee Member




"Precise and accurate localization and motion classification is an emerging fundamental areas for scientific research and engineering developments. Such science and technology began from the broad out door area applications, and gradually grew into smaller and more complicated in-door area and more recently it is proceeding into in-body area networking for medical applications. Localization and motion classification technologies have their own specific challenges depending on the application and environment, which are left for scientists and engineers to overcome. One major challenge is that location estimation and motion classification often use hand-held devices or wearable sensors. Such devices and sensors usually work in indoor, near body environments and the human object has certain effects on the measurements. In that situation, existing mathematical models for general environments are no longer accurate and new models and analytical approaches are required to deal with the human body effects. This has opened opportunities for researchers to tackle a number of demanding problems. This dissertation focuses on three novel problems in localization and motion classification using radio propagation (RF) modeling, in and around the human body. (1) We develop an empirical Time-of-Arrival (TOA) ranging error model for radio propagation from body-mounted sensors to external access points, for human body tracking in indoor environment. This model reflects the effects of human angular motion on TOA ranging estimation, which enables accurate analysis for conventional TOA-based human tracking systems. (2) We use empirical data collected from a RF connection between a pair of body-mounted sensors to classify seven frequently appeared human body motions. This RF based classification approach has enabled health monitoring applications for first responders, hospital patient, and elderly care centers and in most of the situations it can replace the costly video base monitoring systems. (3) We use radio propagation models from body-mounted sensor to medical implants and the moving pattern of micro-robots inside the body to analyze the accuracy of hybrid localization inside the human body. This analysis demonstrates the feasibility of millimeter level of accurate localization inside the human body, which opens up possibilities for 3D reconstruction of the interior of human GI tract."


Worcester Polytechnic Institute

Degree Name



Electrical & Computer Engineering

Project Type


Date Accepted





Motion Classification, Body Area Network, Localization