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I am a PhD Candidate in Department of Information Systems, University of Maryland Baltimore County, currently doing Research Internship at IBM Research, Cambridge, MA. Previously I completed an internship (6 months) at Intel Corporation in Folsom, California. I am also involved with Mobile, Pervasive and Sensor Computing Lab at UMBC. My advisor is Dr. Nirmalya Roy.

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Research Interest

Multi-inhabitant Smart Home Activity Recognition

Cognitive Computing

Affective Computing

Embedded System & Mobile Computing

Sensor Assisted Behavioral Health Analysis


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


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. 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.