Marjorie Skubic, PhD

Professor, Electrical Engineering and Computer Science
University of Missouri


Marjorie Skubic received her Ph.D. in Computer Science from Texas A&M University in 1997, where she specialized in human-robot interaction. She is currently a Professor in the Electrical Engineering and Computer Science Department at the University of Missouri. In addition to her academic experience, she has spent 14 years working in industry as a software developer. Her current research interests include sensor networks for ambient intelligence, preventative health screening and rehabilitation tools, and user interfaces to foster proactive healthcare. In 2006, Dr. Skubic established the Center for Eldercare and Rehabilitation Technology at the University of Missouri and serves as the Center Director for this transdisciplinary team. The center's work supports proactive models of healthcare such as monitoring systems that noninvasively track the physical and cognitive health of elderly residents in their homes and generate alerts for health changes, increasing fall risk, and actual fall events. Recent work has also investigated automated screening of athletes and pianists to flag injury risks, with support for preventative exercises to reduce the risk, as well as rehabilitation support for stroke patients and patients recovering from hand surgery.


Squaring the Life Curve with Proactive Healthcare Technology

Dr. Skubic will describe ongoing interdisciplinary research and sensor technology for ambient intelligence in the home. Longitudinal research studies with in-home sensing systems have shown that smart sensing offers new vital signs for detecting early signs of illness and functional decline. The system provides a new paradigm for proactively managing chronic health conditions, thereby making early treatment possible and helping seniors to maintain health, function and independence. A variety of sensors have been tested, including passive infrared motion sensors, a bed sensor that captures quantitative pulse, respiration, and restlessness while positioned under the mattress, as well as fall detection and gait analysis systems using vision, radar, acoustic arrays, and depth images. Variations of the system have been installed in 15 senior housing sites in Missouri and Iowa as well as private homes in Kansas City, South Dakota, and Columbia, MO, starting in 2005. The latest work includes interfaces for older adults using voice assistant technology and automatic linguistic summaries of sensor data trends. The talk will include an overview of the system and research results as well as case study examples.

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