Context-Aware Multi-Inhabitant Functional and Physiological Health Assessment in Smart Home Environment

摘要

Recognizing the human activity, behavior, and physiological symptoms in smart home environments is of utmost importance for the functional, physiological, and cognitive health assessment of the older adults. Unprecedented data from everyday devices such as smart wristbands, smart ornaments, smartphones, and ambient sensors provide opportunities for activity mining and inference, but pose fundamental research challenges in data processing, physiological feature extraction, activity labeling, learning and inference in the presence of multiple inhabitants. In this thesis, we develop micro-activity driven macro-activity recognition approaches while considering the underpinning spatiotemporal constraints and correlations across multiple inhabitants. We postulate an activity recognition framework that helps recognize the unseen activities by exploiting the underlying taxonomical structure. We also design novel signal processing and machine learning algorithms to detect fine-grained physiological symptoms such as stress, depression and agitation. We combine these activity recognition methodologies along with the physiological health assessment approaches to quantify the functional, behavioral, and cognitive health of the older adults. We collected data from a continuing care retirement community center using our smart home sensor setup. Finally, we evaluate, compare, and benchmark our proposed computational approaches with the clinical tools used extensively for functional and cognitive health assessment in practice.