technology enabled high-touch care majd alwan, ph.d. medical automation research center university...
Post on 27-Mar-2015
222 Views
Preview:
TRANSCRIPT
Technology Enabled High-Touch Technology Enabled High-Touch CareCare
Majd Alwan, Ph.D.Medical Automation Research Center
University of Virginia
Improving healthcare quality and efficiency through the development of advanced technologies
© 2004, Medical Automation Research Center. All Rights Reserved.
© 2004, Medical Automation Research Center
What What isis the MARC? the MARC?
MARC is a research, development and consulting organization providing medical and industrial clients with innovative automation solutions.
Expertise in: Collaborative multidisciplinary research and
development relating to medical care and health issues Automation of healthcare processes Eldercare Technologies Program: Low-cost in-home
monitoring and assistive technologies
© 2004, Medical Automation Research Center
Goals of the Eldercare Goals of the Eldercare Technology ProgramTechnology Program
Provide novel technological solutions:– Improve quality of life for elders/disabled– Multiply caregiver ability to interact positively– Reduce risks and potentially reduce care costs
Launch collaborative research initiatives with interested parties
© 2004, Medical Automation Research Center
Model for Technology Enabled CareModel for Technology Enabled Care
Older AdultData
Service Provider
Adult Child
Physician
ServicesPersonal Health Maintenance
Preventive Interventions
Improved Communications
Inference, Archiving,
and Analysis
© 2004, Medical Automation Research Center
Challenges and ApproachChallenges and Approach
Minimally invasive sensing technology
User Centered design
Completely passive approach– Burden on data analysis
Clinical validation Ability to retrofitting existing
structures Low-bandwidth
– Data reduction at the residence Affordable technologies
Economic impact studies
Privacy and acceptance by older adults and adult children
Acceptance and maximum utility to caregiver
Emphasis on maintaining high touch
Compliance
Early adoption, proliferation, and deployability everywhere, including rural areas
Reimbursement
© 2004, Medical Automation Research Center
Monitoring ADLs: Meal Monitoring ADLs: Meal Preparation and ShoweringPreparation and Showering
Identified minimum set of sensors targeting Activities of Daily Living (ADLs) and developed Inference Rules for
The system was validated through comparisons to a customized PDA activity log in a real “living laboratory” setting:
• No lunch, dinner or showering events were missed by the detection algorithm
• Inference rules are reliable: high correlations between the user’s activity log and the detected activities
© 2004, Medical Automation Research Center
A mattress pad that measures:
• Subject position• Body
temperature• Breathing• Pulse• Movement• Room light
levelProcessed vibration signal gives pulse rate and respiration
Passive Sleep Passive Sleep MonitorMonitor
Subjectposition
Validated Pulse and Respiration against standard clinical measures
© 2004, Medical Automation Research Center
The sensor system shown mounted on the baseboard in walkway path
Detects gait from step-induced floor vibrations
Longitudinal gait analysis and fall detection in natural settings
Passive Gait Passive Gait MonitorMonitor
Original Signal
Falling Person Detected
Assisted Living PilotAssisted Living Pilot
in partnership with Volunteers of America National Services
© 2004, Medical Automation Research Center
Monitoring System OverviewMonitoring System Overview
Data Manag
er
Collects data from multiple sensor units
Data Analysi
s Server
Caregiver / Care Provider
Prepares relevant reports
Sends data log for analysis, update
software
Alert generated
Falls
Sleep/Bed Exit
ADLs
Stove
© 2004, Medical Automation Research Center
- Movement detection – traveling throughout the home space, getting up, moving from room to room, entering/leaving rooms/home, bathroom, shower area, kitchen, living room etc.
– Temperature detection – monitors temperature over cook stove
– Bed sensor – monitors presence and movement in bed, bed-exit and whether heart rate is outside a predefined normal range
Sensor Components Installed in the Sensor Components Installed in the Pilot sitePilot site
© 2004, Medical Automation Research Center
Activities Monitored and AlertsActivities Monitored and Alerts
Optimized the System and the Activities Inference Engine for congregate care settings and augmented with immediate alerting capability
Activities monitored: Bathroom use, Bathing/ Showering, Time in bed, Time out of bed, Movement in bed (restlessness)
Implemented alerts: possible fall, forgotten stove, low pulse and high pulse
The Fall alert was based on bed-exit, inferred from the bed-pad and motion sensing only; no fall event sensor
© 2004, Medical Automation Research Center
Caregiver Report ScreenCaregiver Report Screen
© 2004, Medical Automation Research Center
Restless night
Restful night
Sleep Quality ReportSleep Quality Report
© 2004, Medical Automation Research Center
Pilot Results Pilot Results
• The technology was acceptable to all participants• Common fears among surveyed older adults included fear of falls and not receiving help quickly • Statistically significant (p=0.03) increase in the quality of life of residents after only three months of monitoring, maybe due to increased sense of security• No significant change in caregiver burdens and strains• Somewhat high false alerts rate• Caregivers rely on the reports in care planning
Mean and Standard Deviation
ColumnA B
30
25
20
15
10
5
0
© 2004, Medical Automation Research Center
Future WorkFuture Work
Economic impact assessment studies Larger and longer studies in different care settings Intervention efficacy studies (such as sleep studies) Customize solutions for special populations (dementia
and Alzheimer’s patients) Enhance the alerting sub-system with additional
sensors (e.g. fall event detector) and further refine the rules
© 2004, Medical Automation Research Center
Other Eldercare Technologies Other Eldercare Technologies ProjectsProjects
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20-1.5
-1.0
-0.5
0.0
0.5
2.0
2.5
3.0
3.5
4.0
4.5
-1.0
-0.5
0.0
0.5
Time (Seconds)
Pulse Oximeter Signal
Empirical Technologies CorporationCharlottesville, Virginia434 296-7000
using the ETC Fiber-Optic Loop-back Sensor
Preliminary Heartbeat Detection using a Bathroom Scale
raw ETC Signal Signal
ETC signal FFT filtered from 0.8 - 1.5 Hz
© 2004, Medical Automation Research Center
Thank YouThank You
Medical Automation Research Center
University of Virginiahttp://marc.med.virginia.edu
Majd Alwan, Director, Robotics and Eldercare Technologies ma5x@virginia.edu
Robin Felder, Center Director, rfelder@virginia.edu
top related