Off-campus UMass Amherst users: To download campus access dissertations, please use the following link to log into our proxy server with your UMass Amherst user name and password.
Non-UMass Amherst users: Please talk to your librarian about requesting this dissertation through interlibrary loan.
Dissertations that have an embargo placed on them will not be available to anyone until the embargo expires.
Author ORCID Identifier
N/A
AccessType
Open Access Dissertation
Document Type
dissertation
Degree Name
Doctor of Philosophy (PhD)
Degree Program
Computer Science
Year Degree Awarded
2019
Month Degree Awarded
February
First Advisor
Deepak Ganesan
Second Advisor
Ben Marlin
Subject Categories
Artificial Intelligence and Robotics
Abstract
Mobile health is an emerging field that allows for real-time monitoring of individuals between routine clinical visits. Among others it makes it possible to remotely gather health signals, track disease progression and provide just-in-time interventions. Consumer grade wearable sensors can remotely gather health signals and other time series data. While wearable sensors can be readily deployed on individuals, there are significant challenges in converting raw sensor data into actionable insights. In this dissertation, we develop machine learning methods and models for personalized health monitoring using wearables. Specifically, we address three challenges that arise in these settings. First, data gathered from wearable sensors is noisy making it challenging to extract relevant but nuanced features. We develop probabilistic graphical models to effectively encode domain knowledge when extracting features from noisy wearable sensor data. Second, prediction models developed on one population in lab settings may not generalize to other populations in field settings. We develop domain adaptation techniques to improve lab-to-field generalizability. Third, collecting ground truth labels for health monitoring applications is expensive and burdensome. We develop active learning methods to minimize the effort involved in collecting ground truth labels. We evaluate these methods and models on two case studies: cocaine use detection and human activity recognition.
DOI
https://doi.org/10.7275/13513251
Recommended Citation
Natarajan, Annamalai, "MACHINE LEARNING METHODS FOR PERSONALIZED HEALTH MONITORING USING WEARABLE SENSORS" (2019). Doctoral Dissertations. 1474.
https://doi.org/10.7275/13513251
https://scholarworks.umass.edu/dissertations_2/1474