Date of Award
Doctor of Philosophy
Mobile health devices are now capable of inferring health status, daily behaviors and contexts (e.g., spatio-temporal context) in an individual’s natural environment. For example, sensors embedded in smart phones (e.g., GPS, microphone) and wireless physiological sensors worn on the human body (e.g., ECG, respiration) can continuously monitor an individual’s health, behaviors, and the surrounding environment. Thus, these devices offer a powerful platform for continuously capturing data to precisely understand disease onset and progression, treatment response, and health outcomes through the precise measurement of potential contributors. Promise and potentials of mobile health sensors will be realized only when we will be able to collect good quality of sensor data from user’s natural environment and make meaningful inferences. However, we lack the methods and tools to analyze the quality and quantity of data collected in field, so as to factors that may improve or reduce the quality and quantity of data from mobile sensors that require strict attachment with body. This dissertation proposes an approach to provide visibility into the process of analyzing data yield from wireless wearable physiological sensors deployed in the field environment, which helps identify and quantify major sources of data loss associated with sensor systems and user’s wearing behavior. Finally, this dissertation demonstrates the promise and potentials of returning data back to the study participants via meaningful visualizations of sensor data to promote good quality data collection with mobile health sensors, which aligns with Precision Medicine Initiative (PMI) - a new research effort to revolutionize how we improve health and treat diseases.
dissertation or thesis originally submitted to the local University of Memphis Electronic Theses & dissertation (ETD) Repository.
Rahman, Md Mahbubur, "Improving Quality and Quantity of Data Captured via Wearable Physiological Sensors: A Step Towards Precision Medicine Initiative" (2016). Electronic Theses and Dissertations. 1411.