Download body network - University of Virginia

Survey
yes no Was this document useful for you?
   Thank you for your participation!

* Your assessment is very important for improving the work of artificial intelligence, which forms the content of this project

Document related concepts
no text concepts found
Transcript
Home Health Care and Assisted Living
John Stankovic, Sang Son, Kamin Whitehouse
A. Wood, Z. He, Y. Wu, T. Hnat, S. Lin, V. Srinivasan
Department of Computer Science, University of Virginia
We have developed a wireless sensor network (WSN) called “AlarmNet” for smart healthcare that will open up new opportunities for resident health monitoring in
the home or in assisted living facilities. Real-time (24/7) access to physiological and environmental data is possible. Results demonstrate a strong potential for
improved quality of medical care.
Improve Quality of Life
• Patient Autonomy and Comfort
• Cognitive Assistance
• Nutrition and Hygiene Monitoring
Improve Health Care
• Disease Specific Monitoring
• Monitor Compliance with Treatment
• Smart Clothes - unobtrusive
Resident Health Remote Monitoring
• Real-time and wireless (24/7)
• Long-term for longitudinal studies
Climate Monitoring
• Environmental conditions control
• Pollution detection
Security
• Detection of at-risk medical situations
• Alert triggering
Privacy
• Variable depending on medical situation
Example of a
PDA displaying
accelerometer
data, patient
pulse-rate, and
environmental
temperature.
Pills
Video cameras
Motion sensors
Backbone nodes
Motes (emplaced WSN)
A body network,
embedded in a jacket,
records human activities
such as walking, eating
and stillness using five 2axis accelerometers. It
also incorporates a GPS
to track outdoor location.
Body scale
Pulse-oximeter and
heart rate sensor
Facility map
Harvard University
EKG sensor
MicaZ (Crossbow)
Motion sensors. Located
in each room, they track
the location of the resident
Medical Automation Research Center
Blood pressure and Bed sensor. Based on an
air bladder strip, it
heart rate sensor
records bed movements,
breathing and heart rate
(developed by MARC).
Wearable Body Networks. Collects physiological
data targeted to a particular medical problem.
In the back-end of the system, a medical
application monitors the Circadian Activity
Rhythms (CAR) to extract activity patterns
and detect behavioral anomalies.
MTS 310 (Crossbow)
Indoor temperature
dust, and lumidity
sensors
Wearable
Interfaces:
LCD Mote
Body networks:
Pulse, EKG
Emplaced Sensor Network. It includes
wireless sensor devices deployed in the assisted
living environment (rooms, hallways, units,
furniture) connected to a more resourceful
backbone and the Internet.
Emplaced Sensor
Network
Backbone
Backbone. It connects traditional systems, such as
PDAs, PCs, and in-network databases, to the emplaced
sensor network. Nodes possess significant storage and
computation capability, for query processing and
location services. A template based query system exists
as well as privacy protection.
Back-end Databases. Back-end databases are located
at the control center for long-term archiving,
monitoring and data mining for longitudinal studies.
Caregiver Interfaces:
PC, PDA
Back-end
Database
Wireless Sensor Networks Technology. Heterogeneous power
management depending on the life habits of the resident, topology
management, reliable routing, network arbitration, data aggregation.
Dynamic Privacy. The system monitors and collects patient data,
subject to privacy policies, depending on the current behavioral status
of the resident, and detected anomalies.
Security. Security mechanisms are present throughout the system.
Data association. To know who is doing what in a system where
biometric identification is not always accessible and where multiple
persons may be present at the same time.
Data fusion. Back-end software programs to analyze autonomy,
behavior and health status of the resident.