Context-aware multi-inhabitant functional and physiological health assessment in smart home environment

Abstract

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 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 design novel signal processing and machine learning algorithms to detect physiological symptoms and infer macro-level activity of the inhabitants, respectively. 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. real-time data collected data from a continuing care retirement community center (IRB #HP-00064387) helped us to evaluate, compare, and benchmark our proposed computational approaches with the clinical tools used extensively for functional and cognitive health assessment.