Computational Sleep Science: Machine Learning for the Detection, Diagnosis, and Treatment of Sleep Problems from Wearable Device Data

University of Minnesota Ph.D. dissertation.December 2017. Major: Computer Science. Advisor: Jaideep Srivastava. 1 computer file (PDF); xiii, 122 pages.This thesis is motivated by the rapid increase in global life expectancy without the respective improvements in quality of life. I propose several novel machine learning and data mining methodologies for approaching a paramount component of quality of life, the translational science field of sleep research. Inadequate sleep negatively affects both mental and physical well-being, and exacerbates many non-communicable health problems such as diabetes, depression, cancer and obesity. Taking advantage of the ubiquitous adoption of wearable devices, I create algorithmic solutions to analyse sensor data. The goal is to improve the quality of life of wearable device users, as well as provide clinical insights and tools for sleep researchers and care-providers. Chapter 1 is the introduction. This section substantiates the timely relevance of sleep research for today's society, and its contribution towards improved global health. It covers the history of sleep science technology and identifies core computing challenges in the field. The scope of the thesis is established and an approach is articulated. Useful definitions, sleep domain terminology, and some pre-processing steps are defined. Lastly, an outline for the remainder of the thesis is included. Chapter 2 dives into my proposed methodology for widespread screening of sleep disorders. It surveys results from the application of several statistical and data mining methods. It also introduces my novel deep learning architecture optimized for the unique dimensionality and nature of wearable device data. Chapter 3 focuses on the diagnosis stage of the sleep science process. I introduce a human activity recognition algorithm called RAHAR, Robust Automated Human Activity Recognition. This algorithm is unique in a number of ways, including its objective of annotating a behavioural time series with exertion levels rather than activity type. Chapter 4 focuses on the last step of the sleep science process, therapy. I define a pipeline to identify \textit{behavioural recipes}. These \textit{recipes} are the target behaviour that a user should complete in order to have good quality sleep. This work provides the foundation for building out a dynamic real-time recommender system for wearable device users, or a clinically administered cognitive behavioural therapy program. Chapter 5 summarizes the impact of this body of work, and takes a look into next steps. This chapter concludes my thesis

Enhanced Living Environments

This open access book was prepared as a Final Publication of the COST Action IC1303 “Algorithms, Architectures and Platforms for Enhanced Living Environments (AAPELE)”. The concept of Enhanced Living Environments (ELE) refers to the area of Ambient Assisted Living (AAL) that is more related with Information and Communication Technologies (ICT). Effective ELE solutions require appropriate ICT algorithms, architectures, platforms, and systems, having in view the advance of science and technology in this area and the development of new and innovative solutions that can provide improvements in the quality of life for people in their homes and can reduce the financial burden on the budgets of the healthcare providers. The aim of this book is to become a state-of-the-art reference, discussing progress made, as well as prompting future directions on theories, practices, standards, and strategies related to the ELE area. The book contains 12 chapters and can serve as a valuable reference for undergraduate students, post-graduate students, educators, faculty members, researchers, engineers, medical doctors, healthcare organizations, insurance companies, and research strategists working in this area