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.

普适计算（Ubiquitous computing）构想了一个人们可随时随地获取计算资源的未来世界。近十年来，各类搭载高性能感知、通信与计算能力的微型设备相继问世并得以普及，正逐步将这一愿景变为现实。依托这些感知与计算设备，普适计算致力于为众多具有重大社会影响力的应用领域提供全新且更优的解决方案，涵盖安全、医疗保健、教育、可持续发展、能源及社会信息学等范畴。
本论文研究了普适计算技术如何提供全新解决方案，以变革现有医疗体系，实现个性化医疗，并改善健康人群与临床患者的健康与福祉。论文的第一部分聚焦于基于可穿戴传感器（wearable sensor）的人类活动识别技术——该技术作为支撑各类个性化医疗应用的核心基础技术，包括个人健身监测、长期预防性护理以及针对老年群体的智能照护。第二章介绍了我们基于可穿戴传感器构建的人类活动数据集。第三章至第六章分别提出了四种基于第二章所述人类活动数据集，对人类日常活动进行建模与识别的不同算法。其中，第三章基于特征选择算法分析人类活动信号，证实针对每一类活动单独精心筛选特征可有效提升识别性能。第四章与第五章分别从截然不同的研究视角出发，提出基于字典学习（dictionary learning）与非线性流形学习（nonlinear manifold learning）的新型计算模型，以解决人类活动识别问题。第六章提出了基于近年发展起来的稀疏表示（sparse representation）与压缩感知（compressed sensing）理论的新型活动模型，并证明在此框架下，通过寻找最优特征以实现最佳活动识别性能这一任务的重要性显著降低。
论文的第二部分聚焦于一款名为RehabSPOT的新型可穿戴联网感知系统的设计，该系统用于中风患者的计算机化康复治疗。第七章介绍了RehabSPOT的系统设计，以及其通过实时系统重构实现个性化康复服务的应用价值。第八章介绍了基于可穿戴感知系统的计算模型，该模型可用于分析患者的运动行为，以精准追踪患者在康复过程中取得的进展。