QoLT Featured Guest - Marjorie Skubic-Quality of Life Technology Center - Carnegie Mellon University

Tuesday, March 19, 2013

QoLT Featured Guest - Marjorie Skubic

Proactive Health Management Using In-Home Sensing and Recognition Technology

Marjorie Skubic, Professor, Electrical and Computer Engineering Dept. and Computer Science Dept.
Director, Center for Eldercare and Rehabilitation Technology
College of Engineering, University of Missouri-Columbia

March 22, 2013, 1:00 p.m.-2:00 p.m.
Carnegie Mellon University, Gates-Hillman Center, Room 6115


Abstract

Dr. Skubic will describe ongoing interdisciplinary research investigating the use of in-home sensor technology and machine learning to detect early signs of illness and functional decline, as a strategy towards proactively managing chronic health conditions. The sensor network includes a variety of sensors such as passive infrared motion sensors, a stove sensor, and a new hydraulic bed sensor, that captures quantitative pulse, respiration, and restlessness while positioned under the mattress.  In addition, fall detection and gait analysis systems are being developed using vision, radar, acoustic arrays, and the Microsoft Kinect depth camera. 

The network is being tested in TigerPlace, an aging in place facility in Columbia, MO, designed to help residents manage illness and impairments and stay as healthy and independent as possible.  About 50 sensor networks have been installed in TigerPlace since Fall, 2005, with an average installation time of over 2 years.  More recently, sensor networks have been installed in senior housing in Cedar Falls, Iowa.  Automated health change alerts are generated at both sites and sent to the clinical staff, based on recognized changes in the sensor data patterns.  Gait analysis systems have been installed in 25 senior apartments and are continuously capturing gait through passive observation of residents as they move about the home in their normal activities. 

The talk will focus on challenges in signal processing and machine learning for two sensing systems:  (1) the hydraulic bed sensor system developed at the University of Missouri and (2) the Microsoft Kinect as used for capturing gait parameters from depth images and tracking fall risk.  Case studies will be shown from several senior apartments.


Bio Sketch

marjorie skubicMarjorie Skubic received her Ph.D. in Computer Science from Texas A&M University, where she specialized in distributed telerobotics and robot programming by demonstration.  She is currently a Professorin the Electrical and Computer Engineering Department at the University of Missouri with a joint appointment in Computer Science.  In addition to her academic experience, she has spent 14 years working in industry on real-time applications such as data acquisition and automation.  Her current research interests include sensory perception, computational intelligence, spatial referencing interfaces, human-robot interaction, and sensor networks for eldercare. 

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 interdisciplinary team.  The focus of the Center's work includes monitoring systems for tracking the physical and cognitive health of elderly residents in their homes, logging sensor data and health records in an accessible database, extracting activity and gait patterns, identifying changes in patterns, and generating alerts that flag possible adverse health changes.