Ubiquitous computing for human activity analysis with applications in personalized healthcare

Ubiquitous computing envisions a world in which people can access computing resources anywhere and any time. Over the past decade, the emergence and availability of a variety of miniature devices embedded with powerful sensing, communication, and computational capabilities are turning this vision into reality. Powered by these sensing and computational devices, ubiquitous computing endeavors to provide new and better solutions to problems in many application domains with significant societal impact. These include security, healthcare, education, sustainability, energy, and social informatics. ❧ My thesis investigates how ubiquitous computing technologies bring new solutions to transform the existing healthcare system to enable personalized healthcare and improve health and well-being for both healthy and clinical populations. The first half of this thesis focuses on wearable sensor-based human activity recognition technology which acts as the fundamental technology to support a variety of personalized healthcare applications, including personal fitness monitoring, long-term preventive care, and intelligent assistance for elderly citizens. Chapter 2 presents the human activity dataset we have built based on wearable sensor. Chapter 3 to Chapter 6 presents four different algorithms to model and recognize human daily activities based on the human activity dataset introduced in Chapter 2. Specifically, Chapter 3 analyzes human activity signals based on feature selection algorithms and shows that the recognition performance can be improved by carefully selecting features for each activity separately. Chapter 4 and Chapter 5 discusses new computational models based on dictionary learning and nonlinear manifold learning respectively to solve the human activity recognition problem from a totally different perspective. Chapter 6 presents the new activity model based on the recently developed sparse representation and compressed sensing theories and demonstrates the task of looking for optimal feature to achieve the best activity recognition performance is less important within this framework. ❧ The second half of this thesis focuses on the design of a novel on-body networked sensing system called RehabSPOT for computerized rehabilitation for patients with stroke. Chapter 7 presents the system design of RehabSPOT and its value in personalized rehabilitation delivery via real-time system reconfiguration. Chapter 8 presents the computational model based on wearable sensing system to analyze patients' motor behavior to track precisely the progress patients have made during rehabilitation.