Proactive Health Management Using In-Home Sensing and Recognition Technology

earthblurtingΤεχνίτη Νοημοσύνη και Ρομποτική

14 Νοε 2013 (πριν από 3 χρόνια και 8 μήνες)

92 εμφανίσεις

Proactive Health Management Using In
-
Home Sensing and Recognition Technology


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 proactive
ly managing chronic health conditions
. The sensor network includes
a variety of sensors such as
passive infrared

motion sensors, a stove sensor
,

and a bed sensor that
captures pulse and respiration. 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. Nearly
5
0 sensor networks have

been installed in
TigerPlace since Fall, 2005, with an average installation time of 2 years.

Automated
health
change

alerts are sent to the clinical staff, based on recognized changes in the sensor data
patterns.

In addition, fall detection and
gait analy
sis

systems are being developed using vision,
radar, acoustic arrays, and the
Microsoft
Kinect depth camera. Gait analysis systems have been
installed in
10

TigerPlace apartments and are
continuously

capturing gait through passive
observation of residents
as they move about the home

in their normal activities
.
The team has
recently started installing systems in
senior housing

in Cedar Falls,
Iowa
, with motion, bed, and
Kinect sensors to test health

change

alerts and remote care coordination.
The talk will i
nclude
case studies from several
senior

apartments.



Bio:

Marjorie Skubic
received her Ph.D. in Computer Science
f
rom Texas A&M University
, where she specialized in
distributed telerobotics and robot programming by
demonstration. She is currently a Profe
ssor in 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 E
ldercare 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 res
idents in their homes, logging sensor data in an
accessible database, extracting activity and gait patterns, identifying changes in patterns, and
generating

alerts that flag possible adverse health
changes
.